The Audacity of Hoops

Contributing to SportsIllustrated.com: Charts for Luke Winn's Final Four preview.

2011-04-02T03:38:00.000-04:00

Instead of writing a post this week for Luke Winn's Tourney Blog, I contributed some charts to his massive final Four preview. Click through to the Final Four Intel Report to find what categories the Final Four teams under-and over-performed in during the NCAA tournament, relative to their established rest-of-season levels:

For SportsIllustrated.com: Sweet 16 Matchup Issues

2011-03-24T12:38:00.001-04:00

My latest is over at Luke Winn’s SI Tourney Blog. In it, I use adjusted efficiency data from Ken Pomeroy and adjusted box score data from TeamRankings.com’s possession-based college basketball game simulations to spotlight a few remaining teams that have matchup issues on defense. Summary: FSU should do great against VCU, Duke doesn’t want to see Kansas in the final, and Ohio State’s defense may be slightly overrated.

Catching up on links

2011-03-18T14:16:00.001-04:00

I’ve been delinquent this week in posting links to my articles elsewhere, so here’s a catch up.

On Monday, I had a piece at SI on Luke Winn’s Tourney Blog that I’m rather proud of: a summary of all my defensive charting efforts this year. If you read only one thing I’ve written all year, I hope it’s that.

Tuesday on NBC Sports, I took a look at whether this year’s tournament was expected to be more or less crazy than usual. The verdict was that the first round probably wouldn’t feature any more upsets than usual, but the later rounds should be wide open.

Though I’m not mentioned by name, I wrote the blurbs for the Southeast region for this Tuesday picks post on Chad Millman’s Blog. The NCAA bracket picks themselves were straight from the TeamRankings.com mainframe.

And of course, I’ve had lots of posts over at TeamRankings.com. One nice one from this week examines how much a healthy Kyrie Irving was worth to Duke at the start of this year (which is a different question from how much he’ll be worth in his return, due to issues with his game condition, and amount of practice with the team).

Finally, my “official” bracket is up over at NBC Sports. Full disclosure: I didn’t actually enter this one in any of my pools, but it’s a fairly close copy of my ideal bracket. That said, I have zero confidence in my picks this year. I may or may not do better than these guys:

Please Donate To Japan Disaster Relief

2011-03-15T10:53:00.001-04:00

Jonathan Foster of From The Barn prompted this post. I hope he doesn’t mind, but I’m going to just reprint his plea to support the Japan relief efforts below:

Hello fellow college basketball bloggers,

By now I am sure you are acutely aware of the disaster unfolding throughout Japan. After a massive earthquake, one of the five strongest in recorded history and a subsequent tsunami, there is indescribable devastation. An estimated 10,000 died, thousands more are injured, millions are homeless and a struggling with access to basic needs like food shelter and water, and hundreds of thousands of others have been evacuated from their homes due to the threat of nuclear meltdowns at several power plants throughout the northeast of the country. It is a horrific situation.

Meanwhile, on our side of the Pacific Ocean, we are gearing up for out favorite time of year, the NCAA tournament. Over the next few days and weeks, many of our websites will see the most traffic we see all season. Please consider using this increased traffic to help the people of Japan, and place either a new post asking your readers to donate, or consider adding a link your various open threads, live blogs, game previews, or we got snubbed rants that you'll be posting between now Thursday.

The people of Japan face a monumental effort in rebuilding their country and their lives, and the least we can all do is throw a few links and few dollars their way. Every little bit helps.

For those willing to encourage their readers to help the cause, I'd suggest links to the American Red Cross dedicated Japan Relief Fund, or donating directly to the Japanese Red Cross, made extra easy by the fine folks at google.

Thanks for reading this, and please considering making a donation and making it easy for your readers to donate too.

Sincerely,

Jonathan Foster

Writer and Owner of From The Barn, A Golden Gophers Basketball Blog

This was a classy idea on Jon’s part, and I hope many of you will contribute.

Defensive Score Sheet: Colorado vs. Kansas

2011-03-12T03:56:00.001-05:00

For background on what’s going on here, see my first Project Defensive Score Sheet post.

Here’s the table; notes are after the break.

Only a couple quick notes, since it’s past my bed time:

The Morris twins top the chart for Kansas today almost entirely due to the fact that they didn’t have to guard Alec Burks or Levi Knutson as much as Taylor, Morningstar, and the other smalls did.
Fraction of forced turnovers credited to the action of just one player: 3 of 10 (30%) for Kansas, 4 of 15 (27%) for Colorado. I expected to see Colorado ahead by more here, as they definitely worked harder on the defensive end than the Jayhawks did.
Look at all the turnovers for Colorado’s “Team” defense. Kansas threw it away or walked several times under minimal duress. It was one of the sloppier nights I’ve seen since I started charting. And yet, Kansas still scored 1.25 points per possession. Imagine if they’d have held on to the ball.

Defensive Score Sheet: Oklahoma State vs. Kansas

2011-03-11T04:01:00.001-05:00

I’m back, baby!

OK, so I still have quite a backlog to get through, but I’ve already done the actual charting on a couple of them, and just need to do data entry and formatting. And Saturday looks to be a marathon charting day for the rest.

Here’s the chart for Thursday’s game:

Notes…

From the box score stats, it seemed to be a good defensive game, with both teams under one point per possession. But watching closely, what I saw was a lot of missed shots from both teams. Not forced misses, just decent looks that clanged off the iron. Except when Keiton Page was shooting, of course. That guy made some twirling-and-falling-down shots.
The defensive score sheet matches my impression, as the “Team” defensive lines were two of the better defenders, even with the inclusion of 4 technical foul free throws in Oklahoma State’s “Team” line.
Markieff Morris played splendidly in his little time on the floor. His only negative defensive contribution was the single one of his 5 fouls that sent an OSU player to the line.
After being away from the charts for so long, it’s comforting to come back and find things exactly as they were before: Markieff Morris at the top, and Elijah Johnson at the bottom. I’m still not sold on Johnson as the primary point guard for Kansas.
Several of the Kansas guards we torched by Page. Morningstar’s and Reed’s games look especially good when we have the poor performances of Taylor, Johnson, and Selby as comparisons.
You might notice that this chart shows Oklahoma State with a better defensive rating than Kansas. I believe that has to do with the large difference in free throw shooting between the teams skewing the possession estimates. But to be honest, I’m not positive. I’ll look into it this weekend.
Finally, my usual pet stat, the fraction of turnovers for each team that were caused by the actions of a solo player. My theory is that good defense is accomplished via teamwork, and so team that looks better subjectively ought to have fewer solo turnovers. Tonight’s numbers: 4 of 10 (40%) for Kansas; 5 of 7 (71%) for OSU. I’m not sure which team actually played better D, so hard to say whether this fits the pattern.

For NBCSports.com: Do Conference Tourneys Matter?

2011-03-10T20:04:00.001-05:00

Posted today over at the Peacock: my article that looks at NCAA tournament performance over the past 13 years to determine whether winning your conference tournament is a good thing (higher seed!), a bad thing (tired legs!), or a wash.

For NBCSports.com: BYU, SDSU, and balance

2011-02-25T19:46:00.001-05:00

My latest for NBCSports tackles tomorrow’s BYU-SDSU game, and specifically which of the teams has the balance and interior contributions necessary for an NCAA title run. Hint: The Jimmer doesn’t post up.

Don’t Worry, defense, I haven’t forgotten about you.

2011-02-23T15:36:00.001-05:00

I just wanted to post a quick note to let everyone know that I haven’t abandoned Project Defensive Score Sheet. I have DVR recordings of the last three Kansas games, and I’ll eventually get around to charting them. I’ve just fallen behind because my flesh and blood girlfriend managed to pry me away from the computer long enough to smuggle me into Canada over the long weekend. And I won’t be able to catch up until after this coming weekend, because family’s visiting me in sunny San Diego, and I’d feel like a poor host if I turned them loose on their own just so that I could spend six hours watching basketball games that I already know the outcomes of. So, I’ll probably fall a bit further behind, then catch up in a massive 4-game post sometime next week.

Until then, best of luck to your favorite team.

Defensive Score Sheet: Iowa State@Kansas

2011-02-13T21:26:00.001-05:00

[If you’re unfamiliar with the series, please check out my first Project Defensive Score Sheet post, or else you’re probably going to be very confused.]

Kansas had little trouble with Iowa State, but if you look at the final numbers, you’ll see that the Cyclones managed 0.95 points per possession, which is fine, but not Texas-esque. However, the numbers if the first half were Texas-esque, and with the game basically in hand before halftime, it’s easy to hypothesize that, had the Jayhawks played poorly on offense and thus needed to keep playing good defense, they might have been able to do so. In that light, besides the usual full-game totals, I’m adding a few summary columns for each half, so you can see which players were worse (slacking off?) in the latter period.

Here’s the table. [Apologies to mobile users if this is unreadable, but this was just too large to fit into my usual template.] My comments are after the jump.

Thoughts…

I hate to keep picking on Tyshawn Taylor, but the drop off from half one to half two seemed the most pronounced, subjectively, when watching him. On a couple plays, he floated randomly away from his man, without actually playing any help defense. He also allowed at least one offense rebound and putback when he failed completely to box his man out on the perimeter. Marcus Morris did the same thing.
Judging Taylor based solely on the difference between his first and second half numbers is unfair, however. Notice how much higher his second half defensive usage rate (%DPoss) is. That’s because all Iowa State did in half two (probably because the loss of Vanderbeken thinned out their big man herd) was work to get open three point shots. And they shot very well, so with average luck, Taylor’s (and Elijah Johnson’s) defensive rating would have been a tad better.
Speaking of Johnson, he was yet again Kansas’s worst defender, according to the numbers. And Markieff Morris was yet again at the top of the chart.
Markieff Morris, Jeff Withey, and Diante Garrett were the only players to play consistently excellent defense in both halves.
The whole idea of a nearly-all-garbage-time second half makes me wish all team stats could be adjusted so that anything after a certain blowout cutoff (the Bill James one?) could be ignored. I realize there are all kinds of problems with that, but a man can dream.
Fraction of a team’s forced turnovers which were due to the efforts of just a single player: 8 of 13 (62%) for Kansas, 4 of 8 (50%) from Iowa State. I think this may be the first game that really contradicts my theory that the better defensive effort usually shows up in a higher percentage of teamwork-forced turnovers.
Looking at the defensive ratings (DRtg) for the Kansas players in both halves, you can see how much the play of the rest of the team influences the individual ratings. I’ve begun to almost ignore DRtg in favor of Stop% as a measure of quality, with %DPoss giving me an idea of how much a player was involved.

This is the second KU-ISU game I’ve charted. Here’s a short list of players who have been consistently above or below average in both games.

Kansas, Stop% over 50%: Markieff Morris, Mario Little, Tyrel Reed.
Iowa State, Stop% over 50%: Diante Garrett
Kansas, Stop% under 50%": Brady Morningstar, Elijah Johnson
Iowa State, Stop% under 50%": Jamie Vanderbeken, Scott Christopherson, Jordan Railey, Bubu Palo

Five (And One) For Friday

2011-02-11T21:23:00.001-05:00

No new analysis tonight, but I have a couple posts I want to pass on, so I figured I might as well supplement them with a few more that I’ve enjoyed throughout the week. Most of these are taken from the Daily Feast, which I’ve been writing putting together every morning for the TeamRankings.com blog, so if you followed me over there, a lot of them will seem familiar.

First, the ones directly involving yours truly.

1. Jesse Newell of the Lawrence Journal-World and KUSports.com has apparently been following Project Defensive Score Sheet, because he has compiled the Kansas defensive stats for all the Big 12 games from my charts. It’s something I’d been meaning to do, but kept putting off, usually in favor of, you know, actually watching some basketball. I try to do that now and then. I’m glad he took the initiative, because he did a much more thorough job on the individual player write ups than I would have.

2. This one is a day late and a dollar short. Here’s my third article for NBCSports.com, which previews the two-days-ago North Carolina at Duke game. In hindsight, I think I did a pretty good job. I mean, doesn’t this sound like a bit like a recap of the actual game:

“… the Tar Heels, especially Harrison Barnes, have been playing out of their minds since Kendall Marshall took over at point guard. But that new car smell has to fade away some time, and Cameron Indoor seems a pretty reasonable place for that to happen.”

The Tarheels went to Durham, played out of their minds for a half, and then faded in the second as the Cameron Crazies went, well, crazy. So, maybe a day late and a dollar short was a misnomer. Two days late and a dollar up might be more accurate. Because if you placed a bet on the latest college basketball odds after reading my article, with the way I hyped up UNC, I bet you took the Tarheels and the points (+10). Win for you.

Now, here are a few other stories from this week that I want to make sure you caught:

Maybe the most exciting news of the week took place off the court, with the release of this year’s new and improved NCAA tournament schedule. I agree 100% with this headline.
Like Oregon’s court? Liar. Either way, check out what a similar design aesthetic might have produced at some other schools.
Nathan Walker of The Basketball Distribution created a fun little junk stat called Offensive Decision%. Basically, good actions divided by total actions. Go check out the details. He also came up with a new way of estimating individual defensive ratings from box score stats. To be honest, I haven’t really looked at the equations in depth to see if they check out, so for now I’m taking his word for it. But he’s always right, so that should be fine.
One of my favorite quotes is from the Chaitanya Charitamrita: “Essential truth spoken concisely is true eloquence.” By that definition, this may be the most elegant website in the history of the internet: DidTheCavsWinLastNight.com

Have a good weekend, folks. Tomorrow should be a fun one.

Heady Company

2011-02-10T00:07:00.001-05:00

I don’t usually do link posts, but there’s one making the rounds today that’s worth a mention, for three reasons:

It’s a pretty good list of what somebody thought were the 30 best college basketball blogs, so anybody looking for more daily reading material should make sure they’re already hip to those listed.
The guy who pulled the list together wrote me a polite email about it.

Audacity Of Hoops made it as one of 5 blogs listed in the “Stats” section. Huzzah!

So, head on over to College Crunch, and enjoy.

Defensive Score Sheet: Missouri@Kansas

2011-02-09T23:59:00.001-05:00

If you’re unfamiliar with this series, check out my first Project Defensive Score Sheet post. This one’s a day late, but these numbers are pretty ugly, so I doubt anybody was exactly giddy with anticipation.

Let’s start with the table:

And, my thoughts…

Congratulations are in order to Steve Moore and Kim English, the only two Missouri players who managed to rate above average in Stop%.
The best defender for Kansas? “Team.” Awesome. That essentially means that Missouri shot poorly on their open shots, so Kansas’s bad defensive game could have been even worse.
Tyshawn Taylor’s perimeter defense was pretty bad at times. I recall one play where he was within arm’s length of a Missouri guard, yet stood nonchalantly as the player rose and drained a three. He didn’t even look like he considered raising a hand.
Markieff Morris was the better twin, as seems to be the norm.
Fraction of each team’s turnovers that were created through the efforts of only a single player: 8 of 11 (73%) for Missouri, 2 of 8 (25%) for Kansas.

This is only tangentially related to defense, but since I thought of it, I’m sharing it… one of the reasons Missouri had such a bad defensive game is they everybody on Kansas was hitting threes. Seven Kansas players hit at least one three point shot, and the only players who didn’t were the two that didn’t attempt any: Thomas Robinson and Elijah Johnson. Keep in mind that Josh Selby was sitting injured on the bench. The Jayhawks have 8 players who shoot at least 35% from deep, and average at least 1 3PA per game. They have another guy shooting 53% on 15 attempts (Mario Little), and one more shooting 32% on 25 attempts (Tyshawn Taylor). Really, the only rotation player you can safely ignore is Thomas Robinson.

Nerds of a Feather Work Together

2011-02-07T15:49:00.001-05:00

I don’t know if anybody has noticed my recent more frequent (and during usual business hours) posting, but that was a bit of foreshadowing. I’m excited to announce that, starting today, my primary job for the rest of the college basketball season will be to work with TeamRankings.com. I’ll be doing a lot of behind-the-scenes analysis and marketing work, but you guys will mostly notice that I should shortly begin posting regularly on their blog. I’ll continue posting here in my free time, but in order to read everything I write, I encourage you to bookmark the TR blog, and follow TR on Twitter (@TeamRankingsCBB for college basketball, @TeamRankings for general sports). So, head on over and check TeamRankings out. Here’s a fun place to start.

Defensive Score Sheet: Kansas@Nebraska

2011-02-06T14:48:00.001-05:00

[For a recap of what the numbers below mean, and what this series is all about, see my first Project Defensive Score Sheet post.]

Here’s the score sheet (note that Nebraska’s team line has 10 free throws, because those were clearly strategic end-of-game intentional fouls). My thoughts are after the break.

Kansas handled Nebraska in easier-than-expected fashion, and that can mostly be chalked up to good shooting, and a patient offense that worked to find the best shot. I didn’t try to quantify this, but it seemed to me that Kansas took fewer forced shots this game than in just about any other game of theirs that I’ve watched. Maybe some of that had to do with the absence of Josh Selby, but I think a big factor was that the Morris twins were just too good for Nebraska’s bigs, and the Kansas guards did a good job of realizing this, and running the offense through the post.

It’s interesting to compare these numbers to those from Nebraska at Kansas:

The correlation between Nebraska’s individual Stop percentages in the two games is 0.764 (for Kansas, 0.405). Ray Gallegos, Brandon Richardson, Andre Almeida, and Caleb Walker did well in both game, while Jorge Brian Diaz, Toney McCray, and Brandon Udel were below average in both.
I don’t know what it is about Nebraska, but two of Elijah Johnson’s 3 best defensive games (of those I’ve charted) have come against the Cornhuskers.
Brady Morningstar was great in the first game, but poor this game. (Sorry, I’ve got no explanation.)
The biggest change in Nebraska’s numbers from last game to this one was in the “Team” defense line. Basically, Kansas missed open shots last game, and made them yesterday.

Finally, the fraction of forced turnovers for each team that were solo efforts (my hypothesis being that good defensive teams depend on teamwork, and so will have fewer solo forced turnovers)…

Kansas: 3 of 11 (27%)
Nebraska: 5 of 11 (45%)

Free throws and “Luck”

2011-02-03T15:28:00.001-05:00

In light of today’s great post on Free Throw Plus by John Ezekowitz, I thought I’d share something I noticed a couple days ago. I had heard some Twitter chatter about Illinois consistently ranking low in Ken Pomeroy’s Luck metric, which essentially tells you which teams have tended to be on the right/wrong side of close games. (They’ve fared no better than 158th in the past 7 years.) It made me wonder if they were doing something specific that would lead to their low ranking. So, I decided to see if any of the stats kept by Pomeroy correlate to “Luck.”

Using 2010 team stats, here’s what I found. I highlighted SOS-related stats in yellow, offensive free throw stats in green, defensive free throw stats in red, and bolded tempo. The bars at the right represent the magnitude of the correlation, but keep in mind that the sign also matters:

A few things jump out:

First, how often you get to the line (o_FTR) has a correlation over twice as high as any other non-SOS metric. Obviously you can’t tell the direction of causation (or even if there is any) from the correlation, but my guess is that this means that offensive FTR is the most skewed by close games. Teams that score well in Luck are going to be those that often have small leads late in games. That’s going to cause opponents to intentionally foul them, which will inflate their Free Throw Rate.

Second, the opponent strength metrics have a higher correlation than any stats which measure what happens on the court (other than offensive FTR). These could me symptoms of the same issue: when you are losing a close game, you foul your opponent, which drives up their offensive rating (and vice versa when you’re in the lead). On the other hand, if that’s what’d driving this, shouldn’t we see a similar effect for the team’s own rating?

Third, tempo is relatively high on the list. Again, late game defensive strategy is to foul quickly, extending the game, and leading to more possessions. This, however, should happen whether you’re ahead of behind, so I’m a little surprised that tempo correlates to good luck.

Now, all these correlations are based off of only a single year, so don’t put too much stock in them. Hopefully soon, I’ll get around to checking other years, to find out how consistent this is. But, let’s assume for a minute that A) it is consistent, and B) the close games are causing the correlation with offensive free throw rate. How much of a difference would this make in the numbers? Well, 95% of teams should have Luck values between –0.1 and +0.1. Using a regression line fit to the points from 2010, the difference between those two Luck values should amount to about a 4 point difference in free throw rate.

Getting back to the original subject of Illinois, if we erased all their bad luck (-0.77) you might expect their FTR to rise from 30.5 to 32.1. That only bumps them up from 316th to 296th, so not a huge difference. Still, the effect of shifting late-game strategy on overall team stats is an interesting topic. Hopefully I’ll get a chance to explore it some more in the future.

UPDATE

In the comments, Nathan Walker pointed out that, given the correlation of the team strength metrics to Luck, I ought to do a partial correlation where I hold these values constant. I think he’s right, so I re-ran the numbers, holding the team Pythag and opponent Pythag constant. That was enough to reduce the correlation of adjusted offense and defense (team and opponent) to near zero, while keeping the number of variables small enough to prevent much over-fitting. I also added Free Throw Production [FT% x FTR], at Nathan’s suggestion. The results look similar, and I don’t think my conclusions from above change. In fact, a couple defensive stats that might be affected by end-game defensive strategy (steal rate and opponent eFG%) jump up near the top, which makes sense:

Defensive Score Sheet: Kansas@Texas Tech

2011-02-02T17:44:00.001-05:00

[For explanation of what’s below, check out the original Project Defensive Score Sheet post.]

This one will be quick, as it wasn’t a very intriguing game, and I’ve got a busy day. Here’s the table, notes after the break.

My thoughts…

As usual, the better defensive team used teamwork more often when creating turnovers. Only 2 of KU’s 11 forced turnovers (18%) were solo efforts, caused by a single player, whereas 4 of Texas Tech’s 7 (57%) were solo.
Josh Selby had a great night, at least effort-wise. Besides the stats tracked above, he also ended up in the crowd on three other occasions, after deflecting Red Raider passes.
I am not surprised to see Markieff Morris at the top of the Jayhawks section, and Elijah Johnson at the bottom. I haven’t compiled the numbers yet, but I’m fairly sure that’s where they’ll end up when viewing all the games as a whole.
The spread between Kansas’s best and worst defensive ratings (among those with 10+ minutes) was much larger (17.5) than the spread for Texas Tech (9.9).
Mike Singletary and Robert Lewandowski didn’t get much help trying to handle Thomas Robinson and the Morris twins. They had a lot of field goals allowed (DFGM) where there were no other Tech defenders anywhere close to to the player with the ball. It was especially striking at the start of the game, as even the announcer mentioned it.
Jeff Withey racked up those miserable numbers mostly at the end of the game, with the scrubs in. I’m not sure if that mitigates it (because he had to cover for his poor defensive teammates) or makes it worse (because he allowed points to poor opponent players).
I didn’t keep track of good and bad looks this time, but check out how many of Texas Tech’s defensive possessions were credited to “Team.” Kansas got a lot of open looks.

Defensive Score Sheet: Kansas State@Kansas

2011-02-01T14:05:00.001-05:00

[As always, check out my first Project Defensive Score Sheet post for information on what this all mean. Also, shout out to Ray Floriani for taking a cue from by good/bad shots tracking, and doing it for St. Bonaventure.]

Sorry this is so late. I promise tonight’s game against Texas Tech will be up tomorrow. Here’s the table. My thoughts are after the break.

Before I get to the notes on the score sheet, let me point out something a bit misleading. Jesse Newell of KUSports.com broke down the points per possession of both teams by half:

First Half
KU — 1.06 PPP
KSU — 0.57 PPP
(35 possessions)

Second Half
KU — 1.39 PPP
KSU — 1.18 PPP
(38 possessions)

At first glance, Kansas’s defensive performance in the first half looks amazing, and you figure they let off the gas in the second half. But look at KSU’s shooting by half, broken down into good looks and bad looks, paying special attention to the good looks from 3-point range in the first half:

A huge portion of the difference between the Wildcats’ efficiency in the to halves just comes down to whether or not they hit their open shots. If they shoot a reasonable percentage on their first half good looks, then they score, say, 12 more points, and their PPP is around 0.9. That’s still not great, but it’s a world away from 0.57. Over the course of a season, I imagine this type of thing evens out. But not in one half of play.

Now, check out the same chart for Kansas. What I want you to focus on here is the number of good looks compared to bad looks, for each half:

Kansas State’s defensive just collapsed in the second half, giving Kansas no resistance whatsoever. If they’d have kept up their effort from half one, maybe they’d have had a chance.

OK, notes on the score sheet…

I hate to pick on a role player, but Jordan Henriquez-Roberts enlarged Kansas State’s defensive rating almost as much as he did my chart. He was astoundingly bad, allowing 5 2PFG’s and 4 FTA’s (of which only one was made), while stopping nothing, in only 7 minutes. That’s right, if Marcus Morris and Thomas Robinson had hit their free throws, he would have personally allowed 2 points per minute. Did I mention he also turned it over twice? Frank Martin sat Curtis Martin in favor of this guy?
On the other hand, Martin didn’t sit Rodney McGruder or Jamar Samuels. I doubt it’s a coincidence that these two guys graded out as the best defenders, ignoring the garbage time guys.
In my ongoing test to see if the more impressive defensive team ends up with more of their forced turnovers (FTO) coming from team efforts, here are the numbers from this game. For Kansas, 5 of 15 (33%) of their turnovers were solo efforts. For KSU, that number was 7 of 14 (50%). Once again, team defense FTW, literally.
Once again, Brady Morningstar’s defense is really tough to evaluate. He ends up with a bad rating above, but his man only ended a possession about 6% of the time, meaning he probably played good off-the-ball defense, to deny his man the ball.
Will Spradling, if this game is any indication, loves to try to draw charges, which leads to him also getting called for blocking. If one more of those blocks ends up being called a charge, his defensive rating drops to about 114, which would put him as KSU’s best non-garbage defender.
The Jayhawk starters were the team’s best 5 defenders. I’m sure Bill Self loves that, and expects it.

Time of Possession & single-team pace

2011-01-30T16:17:00.001-05:00

Ohio State’s narrow escape against Northwestern last night presents a great opportunity to talk about an idea that I floated on Twitter a few days ago: keeping track of time of possession (TOP) for basketball teams. That was inspired by an absolutely fantastic post by Rohan Cruyff, who used shot clock data to break down NBA teams’ pace into offensive and defensive “Speed Indexes.” His basic idea: a single number that represents the pace of a team and/or game doesn’t really describe what happens on the court. A fast-paced game can be cause by one team playing at a normal speed, but the other team shooting super early in the shot clock.

Or, as happened last night, a slow-paced game can be caused by one team running the clock down on every possession.

I went through the play-by-play for last night’s game and recorded how much time elapsed on the clock before the first action (shot, turnover, or foul that leads to free throws) of each team’s possession. Here’s what that distribution looked like for each team:

You can see that Northwestern rarely did anything with the ball before burning 15 seconds, whereas most of Ohio State’s possessions resulted in an event around the 16 to 20 second mark. [You might ask why some possessions lasted more than 35 seconds before anything happened. Some of these are due to fouls/kicking that reset the shot clock, and I’m sure some are due to minor inaccuracies in the play-by-play data. Also, Northwestern had a “7-point possession” according to some sources. I broke that down into 2 possessions – before and after the intentional foul.]

I’ve done this for a few other games, and Ohio State’s distribution is pretty close to what seems to be normal. Usually I see a few more possessions in the 6-10 and 11-15 bins, and a few less in the 16-20, but it’s not anything drastic. Northwestern however, is a different story. Their whole distribution is shifted about 10 to 15 seconds to the right.

Northwestern even held onto the ball for a long time after they got offensive rebounds. Here’s a table showing the average possession length for each team, where I count every offensive rebound as starting a new possession:

Ohio State put their offensive boards back up almost immediately, while Northwestern often kicked it back out and reset the offense.

The Wildcats did extremely well their longer possessions. Here are points per possession for both teams, broken down by when the first action of the possession occurred (this time, offensive rebounds are considered a continuation of the original possession, as they usually are):

The higher values for the first 10 seconds are expected, as transition points will pull the averages up. But the high efficiency for Northwestern on very long possessions is unusual. Normally, shots hoisted in the final few seconds of the shot clock are bad ones, forced by a defense that simply won’t let the offense have a good look.

Now, let’s combine those TOP values from the previous table, and get a pace for both offenses. Ohio State had the ball for 14:50 over 48 possessions, which means their average possession lasted 18.5 seconds. Over 40 minutes, that works out to 65 possessions per team. So Northwestern slowed them down a bit, but not all that much. On the other hand, Northwestern held the ball for 25:07 on their 48 possessions, or 31.4 seconds per possession. If both teams played at that pace, they would each get only 38 POSSESSIONS over a 40-minute game. They could average an insane 2 points per possession, and still not crack 80.

So, even pointing out that this was the second slowest major-conference game of the season fails to convey just how glacial Northwestern’s offense was. I know it’s a pipe dream, but I would love to start seeing time-of-possession numbers added to the standard basketball box score. I mean, ideally, I’d want even more than that (like the distribution of possession times that I showed above), but baby steps, right? TOP seems like a good place to start, because it’s already familiar from football, and frequently-cited even by “traditional” media.. It won’t be looked at as some “advanced” statistic dreamed up by stat heads, so it shouldn’t have as much of a barrier to acceptance.

Who’s with me?

Defensive Score Sheet: Kansas@Colorado

2011-01-27T18:22:00.001-05:00

For those of you new to this series, check out the original Project Defensive Score Sheet post. The rest of you know what I’m about to say…

“Here’s the chart (click to enlarge). Notes are after the break.”

The first thing that jumps out at me here is the girls-and-boys-on-opposite-sides-of-the-gym … –ness of Kansas’s numbers. Tyrel Reed was the only small better than the “Team” line, and Markieff Morris was the only big worse, with tweener Mario Little sitting right in the middle of them. That prompted me to run a Bigs/Smalls split (I gave half credit for Little to each group):

I’m willing to bet that has a lot to do with the smalls getting stuck guarding Cory Higgins and Alec Burks, while the bigs got Marcus Relphorde and Austin Dufault. That illustrates one of the biggest weaknesses in this project – lack of matchup info. Even among the smalls, as Tyrel Reed the best defender because of actual play, or because he got to guard the fifth wheel more often? Sorry, I don’t have the answer.

Here’s another split that raises questions:

According to my notes, all of Colorado’s three point shots were good looks. That can’t be right, can it? I’m searching my memory, but I can’t seem to refute the statement. And I already erased the DVR recording. Anybody who watched want to chime in and set me straight? If true, it explains why they shot so well from deep. And check out Colorado’s 2PFG% on good looks. Killer. I’m not 100% sure I trust these numbers, though.

And, finally…

As usual, let’s look at the fraction of turnovers that were caused by solo efforts. For Kansas, 0 of 6. For Colorado, 12 of 13 (92%). This is the starkest difference I’ve seen so far. And again, I think the team that played better defense ended up with more teamwork-caused FTO’s.
Cory Higgins’s feast or famine defensive night was partly caused by his tendency to provide a lot of help defense, so much that he sometimes left his man with open looks from three.
There a couple of terrible calls that went against Markieff Morris, turning a forced turnover into foul shots, and stealing a defensive rebound from him. Reverse those, and his defensive rating is 111.4, back in the top half where he usually sits.
Also back to their usual spots: Tyrel Reed at the top, and Elijah Johnson in the bottom.
And, while Josh Selby had a good game offensively, he didn’t do well defensively. He’s been very erratic, as I guess you should expect from a freshman.

Defensive Score Sheet: Texas@Kansas

2011-01-26T18:30:00.001-05:00

Better late than never, right? Being a Kansas fan, this wasn’t a fun one to score. Here’s the chart, notes are after the break.

Announcer comments take on a whole new meaning when you know the eventual outcome. I have to give Clark Kellogg props for something he said during Kansas’s early run that foreshadowed Texas’s eventual comeback:

“The Longhorns have gotten 3 good shots, they just haven’t hit them. Kansas has gotten 3 good shots, and they haven’t missed.”

The implication was that Texas wasn’t really playing that bad, and they’d be fine if they started hitting shots. Well, they started hitting shots, and Kansas started missing.

I kept track of whether each 3-pt shot was a “good look” or a “bad look” based on how close the defender was, and whether the player was able to set himself. That’s very subjective, but I think my judgments at least remained pretty consistent throughout the game, and for both teams, so this chart should be valid. [NOTE: I show one extra 3PFGA for Texas. That was the Balbay play at the buzzer, where he brought the ball up, and Morningstar knocked it out of his hand. It’s not listed in the ESPN play-by-play, but I counted it as a shot defended.]

The number of good 3-point looks allowed by each team didn’t change from half to half. Yet Kansas outscored Texas by 9 on threes in the first half, and were outscored by 6 in the second. Sometimes you eat the bar, etc.

The story is similar for shots taken inside the arc. Here, I tracked whether there the player was being closely guarded when he made the shot. Using a nice post move to spin around your defender and lay it in counts as closely guarded. Catching a backdoor pass and laying it in doesn’t. [Again, my numbers are off my an attempt or two, but it shouldn’t change the overall picture.]

The difference from half to half isn’t as sharp, but again, Kansas shot worse on their open shots, and Texas shot better on their open and guarded shots in half two. One additional difference is that, surprisingly, Texas got fewer open shots. From these two shots, it actually looks like Kansas created harder shots for Texas in the second half, yet Texas made more of them. This leads me to another quote from Mr. Kellogg, uttered with about 4 minutes left on the clock, which I think concisely sums up why Texas won the game, and why they’re a better team:

“Both teams have defended. Texas has playmakers.”

OK, on to a few random notes…

I included the technical foul free throws, and the “we have to foul whoever has the ball” end-of-game free throws, in the “Team” lines, as opposed to the actual players who caused the free throws.
Gary Johnson may not have the best defensive rating for Texas, but he was an absolute beast inside. When you take into account his shared credit on some plays, he was involved in forcing a miss on 13 Kansas shots. They only missed 41 all game, which means every time KU missed a shot, there was about a 1 in 3 chance that Johnson helped cause it. (And he didn’t even play 40 minutes).
Here are the percentage of each team’s Forced Turnovers (FTO) which were solo efforts, entirely credited to one player: 6 out of 15 (40%) for Kansas, 4 of 12 (33%) for Texas. My hypothesis at the beginning was that the better defensive team would force more turnovers through teamwork (i.e. have a lower % due to solo efforts), and that seems to be holding true so far.
It was a bad day for Markieff Morris to get in foul trouble, as Thomas Robinson, understandably, wasn’t himself. Markieff defended well while he was in the game.
For that matter, so did Jeff Withey. I’m not sure why didn’t get at least a few more minutes. Mario Little was the 2nd biggest player on the floor for Kansas for a couple stretches in the first half, and as you can see from his defensive stats, he couldn’t handle the task. Withey seemed to do at least a passable job.
Tyrel Reed looked comically mismatches when guarding Jordan Hamilton on a few possessions, but he still had a decent game, defensively. Morningstar, who covered Hamilton for longer stretches, did not, mainly due to a few fouls.
The game was won by Texas in the second half, so I split out the stats by half. I’m not going to show the whole charts, but I will say that the best defenders, statistically, for Kansas in the second half were Tyrel Reed and Marcus Morris, while the worst – by a mile – were Brady Morningstar, Josh Selby, and Mario Little. All three of them had a Stop% under 10%, which is abysmal. Part of that is probably due to matchups, of course.

Finally, I want to pass on a link to a fellow defensive charter. Jonathon Leverenz over at A Sea Of Blue also read Basketball On Paper, and was thinking of charting games, but hadn’t gotten motivated enough to do it. I think he saw that a Kansas fan was charting, and couldn’t let me “win,” so to speak, so he’s now producing defensive score sheets for Wildcats games. Here’s his chart for Kentucky at South Carolina.

For NBCSports.com: Player of the year hype clouds

2011-01-25T16:08:00.001-05:00

My second article for ‘Peacock Sports’ just went live. This one applies my hype clouds idea to the Player of the Year race, and looks at how hype matches up with actual offensive production for the three main candidates (Walker, Fredette, and Sullinger). Here’s a sneak peak at the January POY hype cloud:

To read the whole thing, head over to NBCSports.com

In Search of: The First Six Minutes Of Texas@Kansas

2011-01-23T15:49:00.001-05:00

[EDIT: I’m no longer searching. Huge thanks to Luke Winn, who probably has access to some super secret SI footage vault, for sending me video of the first half. I should get this game scored tonight, just in time to watch Kansas@Colorado.]

Here on the west coast, CBS preempted the beginning of the Texas-Kansas game with 10 minutes of UCLA shooting free throws to close out their win over Stanford. I don’t really want to post the defensive charting for a partial game, so the Project Defensive Score Sheet stats will be delayed until I can find a source for the opening 6 minutes. Or until I give up. If anybody has a link to an online replay stream, or if you want to take a video of the beginning off of a DVR recording, let me know in the comments (or drop me an email). Thanks!

Over The Back? What Over The Back? [Or: Minute-Adjusted Tempo-Free Rebounding Leaders]

2011-01-19T20:12:00.001-05:00

[EDIT: I made a mistake in the original description of how to calculate OffReb+. I originally wrote that I multiplied by 70.2, but that’s incorrect. 70.2 is the average total rebounds in a game, for both teams, so I actually multiplied by half that (35.1) when I did the calculations. I just had a senior moment when I wrote it out. Sorry about the confusion. The reason I noticed in the first place is that Mike Rogers over at The Only Colors has a couple nice posts up that expand on this idea (here and here). Highly worth checking out. I noticed his numbers didn’t jive with mine, and that’s how I spotted the error in the text. So, thanks Mike!]

In case you missed it, Tennessee’s Brian Williams made what ESPN calls a “falling over-the-back buzzer beater” last night against Georgia. Bulldog Chris Barnes did a good job of boxing out, but Williams simply reached over his shoulder and snatched ball. The play reinforced a thought that’s been rolling around in my head: that instead of merely praising players with high offensive rebounding rates (whether measured by raw numbers or by offensive rebounding percentage [OR%]), we ought to be praising those that can do it without fouling. It’s not that the fouls themselves are so detrimental – if a player dials back the physicality enough to prevent the foul, he likely prevents the offensive rebound as well. But racking up fouls on high-risk offensive boards leads to reduced minutes for a player that provides at least some value in the form of rebounds.

Take a look at the current top ten offensive rebounders, by OR%, along with their fouls committed per 40 minutes (FC/40). Over half of these guys would foul out if asked to play play 40 minutes:

Hey, look – Brian Williams!

I thought it would be interesting to look at top ten that takes minutes played into account. This should penalize the foulers, plus it should knock out any small sample size wonders.

So, here’s what I did. From team stat pages on kenpom.com, I see that centers account for 32.7% of offensive rebounds, and power forwards for 29.3%, with a steep drop off after that. I chose 30% as the share of team offensive rebounds for an average C/PF. Combining that with the average team OR% of 32.6% gives me a typical big man OR% of 9.8%. Subtracting that from a player’s actual OR% gives his OR%-above-average. I multiply that by 35.1 (half of the average number of total rebounds in a game this year according to StatSheet) to get his adjusted offensive rebounds above average, per 40 minutes. This number then gets multiplied by his percent of team minutes played, and I’m finally left with a value that represents the number of offensive rebounds the player gains you per game, over an average big man, assuming his typical playing time, in a game which has an NCAA-average number of rebounds available. I’ll call it OffReb+.

Here’s the OffReb+ top ten:

It’s never a surprise to see Kenneth Faried top any list relating to rebounding, but his lead here is huge. Nobody else comes close to combining his OR% with a low foul rate and high minutes played.

Look how far Rhamel Brown rose, just by being able to stay on the floor for significant minutes. Also note that a lot of low-minute guys are still on here, partly because there just aren’t many players that can pick up offensive rebounds without fouling.

To look at defensive rebounding, I used the same logic as above, but plugged in the appropriate defensive rebounding numbers, and come up with a DefReb+ top ten:

There’s Kenneth again, though with a smaller lead. That makes sense – when some teams don’t even bother to crash the offensive glass, everyone will do well against them, making the top talents stand out less.

This list doesn’t suffer at all from the fouls problem that plagues the offensive rebounding leaders, which also makes sense – defensive rebounding is generally considered to be about maintaining established position, instead of the aggressively trying to overturn that positional hierarchy. With one guy standing still, and one guy trying to go around or through him, who’s going to end up on the right side of the whistle?

Well, last night it was the guy going over the back, but you get the idea.

Project Defensive Score Sheet: Kansas@Baylor

2011-01-18T22:06:00.001-05:00

[Check out the introduction to Project Defensive Score Sheet for info on what the numbers below mean.]

Let’s start with the chart (click to enlarge). Notes after the break.

Though not shown above, I kept separate tallies for 2PFGAs and 3PFGAs, and tried to to track how closely guarded the defender was. Unfortunately, I realized after the game that my method of marking the 2PFGAs was ambiguous, so I had to throw those numbers out. But here’s what I have for three-point shots:

“Semi-guarded” means a defender tried to close out on the shooter, or was in the area, but didn’t get close enough to have much effect on the shots (obviously a bit of a judgment call).

Nothing groundbreaking here – guarded threes are tougher to hit. Duh. But it’s interesting to see the breakdown. Baylor seemed to do a better job of only taking the 3’s that were given to them, and not forcing any.

Other notes on the defensive data:

For the 3rd time in 4 games, Elijah Johnson is Kansas’s worst defender.
Baylor’s zone was effective not at stopping Kansas, but at spreading the blame for the bad defense around equally.
Marcus Morris finally rated better than his less heralded brother, Markieff (though Markieff still led the team in Stops).
Brady Morningstar and Tyrel Reed both had their worst performance of the 4 charted game, but both have excuses. Morningstar shadowed LaceDarius Dunn for most of his minutes (and was the victim of a couple of very questionable foul calls). And Reed, bizarrely, was matched up on 6’10” Anthony Jones on several possessions, which led to at least one of his fouls and allowed field goals.
Only 5 of Kansas’s 18 forced turnovers (28%) were solo efforts, compared to 5 of Baylor’s 14 (36%), meaning Kansas once again relied mores on teamwork to force their turnovers, compared to opposition. The one game so far where that hasn’t been the case was against Nebraska, who I thought played a phenomenal defensive game.
The 25.5 %DPoss of Baylor’s “Team” line is the highest I’ve encountered so far. That reflects two things. 1) Kansas was very sloppy with the ball early, committing several unforced turnovers. 2) The zone was late to rotate on a lot of plays, leading to wide open threes or dunks.
There were, supposedly, 10 NBA general managers and 30 scouts in attendance. Maybe that fired up Josh Selby, because he played with a ton of energy on defense, and was involved in 7 different turnovers (3 solo, 4 shared).

Project Defensive Score Sheet: Nebraska@Kansas

2011-01-16T15:33:00.001-05:00

[For background info, see my original Project Defensive Score Sheet post.]

When I started this project, and saw that the Jayhawks’ first three opponents were Michigan, Iowa State and Nebraska, I figured the defensive charting would be what helped me stay interested in what I assumed would be fairly easy victories. Not so much.

Nebraska’s first half defense was the most impressive I’ve seen from any unit in the few games that I’ve charted. The help was coming at the exact right time, and recovery back to shooters was great. To be honest, I’m not sure whether Kansas made adjustments at halftime, or if Nebraska just didn’t quite play as well, but my subjective “Wow, these guys are good” feeling wasn’t nearly as strong in half two. I guess allowing 4 straight dunks will do that.

Here are the numbers (click to enlarge). Notes after the break.

First, let me point out the 2 free throws credited to Nebraska’s “Team” line. These resulted from a foul by Caleb Walker with 14 seconds left. It was clearly a case where whoever was near the ball handler after the inbounds pass was supposed to foul, so I didn’t think it was fair to credit the free throws to Walker. His DRtg becomes 95.5 if you add those free throws to his stat line.

My thoughts:

Brady Morningstar lived up to his reputation in this one. I think a couple of his forced misses (FM) were a little bit more on the shooter than on his defense, but he still gets credit in the numbers. Note that his %DPoss in the three games has been 12.9, 12.8, and 11.1. I suspect this may be an indication that he does a good job denying his man the ball (and/or open shots).
Elijah Johnson looked good in this one, unlike the last two games. I think the main difference was that he didn’t commit any fouls, and hence gave away 0 free points this time.
Markieff Morris had KU’s 2nd-best defensive rating for the 3rd game in a row.
Marcus Morris fouled a 3-pt shooter in the closing minutes for the second game in a row. I said it didn’t matter last time, but I probably should have qualified that with “to the final outcome.” It does matter if he has a tendency to make these fouls.
Brandon Richardson was a tremendous pest. I credited him with 4.8 forced turnovers (FTO), but since some of those were shared, he actually played a role in 8 of Kansas’s 15 turnovers.
Fraction of a team’s forced turnovers that I credited as solo efforts: 50% for Kansas (7 of 14), 40% for Nebraska (6 of 15).
Note the 11 “Team” forced misses by Nebraska. Most of these were three-pointers. The fact that Kansas missed a lot of these probably heavily influenced my subjective view of Nebraska’s first half defense as “impressive.”
Nebraska pulled out a zone for a few possessions in the second half. After Kansas’s struggles against Michigan’s zone, I didn’t expect them to fare well, but by my count they scored 7 points on 5 possessions against the zone – far better than their efficiency against Nebraska’s man-to-man defense.

It’s All Downhill From Here

2011-01-14T19:52:00.001-05:00

Just a short “Cool Factoid” kind of post…

Boston College is sitting at 3-0 in ACC play, despite playing absolutely no defense. Given their lack of turnovers, rebounds, fouls, steals, and blocks, their team motto must be “Please refrain from rudely jostling the gentlemen in the opposing jerseys.” Unsurprisingly, Pomeroy’s projections have them finishing at only 9-7, meaning they’re predicted to sputter to a 6-7 record from here on out.

Out of curiosity, I thought I’d look up who else is in for a rude awakening. Let’s define that as:

Currently at least 2 games above .500 in conference play
Projected to have a losing record in conference from this point forward

Here’s the complete list, in approximately increasing order of future fan disappointment:

Cal Poly (currently 3-1, projected 5-6 in remaining games)
Boston College (3-0, projected 6-7)
Idaho (4-1, projected 5-6)
Detroit (4-1, projected 6-7)
James Madison (4-1, projected 6-7)
Florida International (3-1, projected 5-7)
Tennessee Tech (4-2, projected 5-7)
Colorado (2-0, projected 5-9)
Fresno State (3-1, projected 4-8)
Penn State (3-1, projected 4-9)
Louisiana State (2-0, projected 4-10)
George Washington (3-0, projected 4-9)
Binghamton (3-0, projected 3-10)
Chattanooga (5-0, projected 4-9)

I feel bad for Chattanooga coach John Shulman. His teams seem bipolar:

2007: started 0-5, finished 6-7
2008: started 7-0, finished 6-7
2009: started 0-3, finished 11-6
2010: started 3-1, finished 3-11
2011: started 5-0, projected 4-9

Maybe it’s the scheduling.

Anyway, if you live near one of these colleges, brace yourself for some future “What’s wrong with X?” treatises from your local columnists. Or find some homers you can make some profitable futures bets with.

Project Defensive Score Sheet: Kansas@Iowa State

2011-01-13T20:07:00.000-05:00

Here’s Game 2 of my stab at charting individual defensive stats. Check Game 1 for an introduction and explanation, otherwise you’ll have no clue what the alphabet soup is trying to tell you.

Before I get to the numbers, I have to complain about the TV coverage last night. There were multiple times where I missed a turnover or shot attempt because the producers decided to show close ups of bench players celebrating a dunk, or something along those lines. And then the announcers failed to clue me in on what had just happened (“Great play there by Iowa State” isn’t very informative, guys). So, my stats didn’t quite line up with the box score at the end of the game, and I lumped the ~5 plays I was missing into the “Team” lines. Ugh.

More notes and comments are after the break, but first, the tables (click for larger view):

I’m trying an alternate sorting this time: by defensive rating (DRtg) rather than number of defensive possessions (DPoss). I think this makes it easier to see at a glance who defended well, regardless of how long they were on the floor. Then, I can mentally take minutes and %DPoss into account when evaluating overall defensive value.

Thoughts:

Elijah Johnson was the worst defender for Kansas for the second game in a row.
Check out Calvin Godfrey and Jordan Railey, the worst two defenders for Iowa State. When I first transcribed the data from my handwritten chart, I thought I must have made a mistake. 13 free throws caused in only 12 combined minutes? But I’m pretty sure it’s legit – they committed 9 fouls in their 12 minutes. I don’t know what’s more impressive – fouling out in only 9 minutes (Godfrey), or ending up with more fouls than minutes (Railey, with 4 fouls in 3 minutes). I took a few notes while scoring, and next to Railey’s name I wrote “can’t handle Morrii FFF”.
Kansas did a fantastic job of getting the ball to the Morris twins to attack Godfrey and Railey while they were in. Check out those %DPoss values – 33.9% for Godfrey, and an astounding 65.8% for Railey. Given the 5 point final margin, you could make a pretty good claim that this was what won the game for the Jayhawks.
Not to keep railing on Railey and Godfrey, but compare them to Vanderbeken, who was the main interior defender for Iowa State, and hence bodied up on a Morris on nearly every play. Vanderbeken is a senior, and the other two are freshmen, and it showed. Vanderbeken didn’t have a great game, but he had a tremendous amount of defensive responsibility (28.4 %DPoss), and he could have done a lot worse.
3 of Marcus Morris’s 5 made free throws allowed (DFTM) were the result of fouling Vanderbeken while he was shooting a three-pointer with only 3 seconds left, with ISU down by 7. It was a dumb foul, but made no difference in the outcome. If you remove that play, Morris’s rating changes from 103.3 to slightly more respectable 100.7.
Markieff Morris was again one of the top defenders for the Kansas, and better than his brother Marcus. I’m curious if this is the usual result.
Similarly, Tyrel Reed’s DRtg was again among KU’s top 3, despite the fact that he again ended up with more official steals (2) than my unofficial forced turnovers (1.8).
Diante Garrett’s %DPoss was a ridiculously low 3.4%. The announcers told me about a dozen times that he’s the only Cyclone who can create his own shot, so my guess is that Mayor Hoiberg is intentionally having him guard whichever opponent player is least active on offense, to save him from fatigue and fouls. I noticed him shadowing Brady Morningstar on several possessions.
I’ve been impressed by what I’ve seen defensively from Josh Selby both games. He’s quick, with active hands. He does seem to stray a bit to far from his man at times, and ends up closing a touch late, but it hasn’t been too damaging in these games.
Note that Iowa State’s “Team” was one of their top defenders. I think this is a sign of how sloppy Kansas played, offensively. There were quite a few missed open shots, and unforced turnovers. (However, remember that a few of these were simply plays that I missed because of the poor TV coverage.)
Kansas’s forced turnovers were again mostly team efforts. I only credited 2 of 12 (17%) as solo efforts. For Iowa State, that number was 7 of 16 (44%).

For NBCSports.com: Purdue’s Life After Hummel

2011-01-13T10:21:00.001-05:00

In what to me is an awesome piece of news, I’ve been invited by Mike Miller to write a few articles for NBCSports.com this season. The first one went live yesterday, and takes a statistics-oriented look into the question of how Purdue has managed to play like an elite team this year without Robbie Hummel, after struggling mightily without him at the end of last year.

As a teaser, here’s one of the tables from the article, showing that E’Twaun Moore was forced to carry a ridiculous load after Hummel went down last season, and struggled to do it. But he’s been able to dial his Shot% back down to normal-for-him this year, and has seen his offense numbers bounce most of the way back to their 2010 pre-Hummel-injury levels:

To read the rest of the piece, head over to NBCSports.com.

Project Defensive Score Sheet: Kansas@Michigan

2011-01-11T19:36:00.001-05:00

As I mentioned a couple weeks ago, I just read (most of) Dean Oliver’s Basketball On Paper for the first time. A few of the chapters focus on defense, and there’s one specifically about individual defensive stats. Oliver laments the lack of useful defensive data, and talks about a WNBA pet project of his: Project Defensive Score Sheet. This was (is?) an effort to keep track of individual defensive credit or blame for all of the shots and turnovers a team allows. After reading the chapter, I rushed to the internet in hopes of finding the results of his efforts, but was disappointed to find very little information available, and very few people attempting to continue or expand on the project. [See the end of this post for a list of what I did find.]

I thought it would be fun, and possibly illuminating, to keep track of Oliver’s individual defensive stats for some college basketball games. In a happy little coincidence, the top defensive team in the country happens to also be my favorite team, so I decided to try to chart as many Kansas games as time and TV coverage would allow, starting with this past Sunday’s game against Michigan.

It’s amazing how different the game-watching experience was when I was forced to pay close attention to defensive players. I would never have otherwise noticed how active – and cagey – Jordan Morgan is. More on that later. Anyway, at this point, I don’t have any brilliant ideas on what to do with the numbers, so I’ll simply start by posting the results of each game.

Here’s the table (click for larger view); the explanation is after the break.

DEFINITIONS

Taken from the box score
- Min – Minutes played
- DREB – Defensive REBounds
Tracked by me
- FM - Forced field goal Miss – when a defender forces an offensive player to miss a shot from the field. Oliver separates FM from Blocks, but I’ve lumped them together here.
- FTO – Forced TurnOver – when a defender forces an offensive player to turn the ball over. Again, Oliver separates out Steals, but I’ve combined them, partly because because I don’t know which plays the official scorekeeper would actually count as steals. One thing to note here is that a player who draws an offensive foul is always credited with a FTO, even if it’s just a moving screen.
- FFTA – Forced missed Free Throw Attempt – missed foul shots resulting from a defender’s foul
- DFGM – allowed Defensive Field Goal Made – when a defender allows an offensive player to score a field goal over him or by dribbling by him
- DFTM – allowed Free Throw Made – made free throws resulting from a defender’s foul
Calculated Tallies
- Stops – the credit a defensive player gets for actions that contributed to ending an opponent possession. This isn’t as simple as adding FM + FTO + 0.4*FFTA, because the credit for a missed shot has to be shared with the defensive player who rebounds it. The formula is more complex than you might think, and includes a sliding weight for FM vs. DREB, based on how difficult those actions seem to be in each particular game, so I’ll just refer you to Appendix 3 of Basketball On Paper.
- ScPos – Scoring Possessions allowed by a player. This is essentially just DFGM plus a FT-related factor. I’ll again refer you to Basketball On Paper for details.
- DPoss – [Stops + ScPos] – total Defensive Possessions that were credited to (or blamed on) a player.
Calculated Metrics
- Stop% – [Stops/DPoss] – the fraction of an individual player’s credited defensive possessions that ended with 0 points. Essentially the inverse of offensive Floor%.
- %DPoss – [(Min/40)*DPoss/TeamDefensivePossessions] (for a non-OT game) - the percentage of team defensive possessions faced by an individual defender. Analogous to %Poss on offense.
- DRtg – [(1–%DPoss)*TeamDRtg + %DPoss*(100*TeamDefPtsPerScPoss*(1-Stop%))] – individual Defensive Rating. Gives a player credit for stops and scoring possessions he was directly involved in, then assumes a nebulous team-average performance in the other possessions. This is the analog of offensive rating.

A FEW NOTES ON THE DATA COLLECTION PROCESS

1. I’m sure you spied all the decimals in the “tracked by me” stats. Why are they there? Well, because defensive actions aren’t nearly as discrete and well-defined as offensive ones. The player who touched the ball last gets 100% of the credit for a made basket, but who gets credit for forcing a turnover when a two-man trap causes a player to travel? I gave each trapping player half credit, because that seemed to be Oliver’s original intent. But I found some plays where even that didn’t cut it. I can’t remember the specific players involved at this point, but there was one play on Sunday where a Michigan player dribbled into the lane and was immediately confronted with a two-player wall blocking him from the basket. He turned to his right, ran into a third Kansas player, bounced backwards and turned further rightwards, and a fourth KU player stripped him of the ball while he was concentrating on not falling over. I’m fairly sure the official scorer gave Jayhawk #4 a steal, but I split it 4 ways.

2. Speaking of the official steal being credited to only one player, Tyrel Reed’s official line from Sunday gives him 4 steals, while I only give him 2.6 forced turnovers. I’m guessing the play above accounts for 0.75 of that difference, but that still doesn’t get us all the way there. I believe there was another play where a Michigan player simply threw the ball past his teammate, into Reed’s hands. If Reed hadn’t been standing there, the ball would have gone out of bounds – Jayhawk ball either way. I credited that play as a “team” forced turnover. If an opponent takes a shot or makes a turnover with no player defending him, it’s credited to the team as a whole.

3. As the above notes indicate, these numbers are extremely subjective. Luckily, changing a couple plays doesn’t make as much difference as you might think. Sticking with Tyrel Reed, if I change his 2.6 turnovers to 4, his DRtg changes from 76.9 to 75.0. Yes, that’s a noticeable difference, but it won’t change a good game to a bad game, or vice versa. However, the subjectivity makes it unlikely that I’ll try to do any real analysis on these. (That, plus I’m not recording WHO took the shot, which is pretty important.)

AND A FEW – HOPEFULLY MORE INTERESTING – NOTES ON WHAT THE DATA SHOWS US

1. You can see above that about 12% of Kansas’s defensive possessions, and 10% of Michigan’s, were ended by the action of an unguarded player. You can also see that Michigan made 5 of 6 unguarded shots, while Kansas only made 3 of 7. Both teams committed 3 unforced turnovers. I’m curious to see this numbers going forward, since it seems like a low fraction of FGA credited to “Team” is a sign of good defense.

2. Looking at Kansas’s individual stats on kenpom.com, you might wonder why Elijah Johnson isn’t getting more playing time, as he has the highest offensive rating on the team. Well, this is only one game, but the fact that his Stop% was lower than the unguarded “Team” number can’t be a very good sign.

3. Notice that Brady Morningstar had the lowest %DPoss among Kansas players. He’s generally regarded as one of the team’s best defensive players, but he didn’t seem very involved this game. I have a feeling that might be misleading. A good off-ball defender may be underrated by these numbers, because ball denial should result in a lower %DPoss, meaning more of the player’s DRtg will come from the team-average portion of the calculation.

4. Kansas’s highest %DPoss players were their worst 2 defenders, while Michigan’s highest %DPoss players were among their best. Just throwing that out there.

5. This doesn’t show up in the above table, but it’s amazing how many of Kansas’s forced turnovers were the result of teamwork. Only 3 of 14 turnovers were solely credited to one player, compared to 9 of 14 for Michigan. I’ll be curious to see if this trend continues. I would assume (with absolutely nothing to back this up) that better defensive teams would have more shared credit on both shots and turnovers.

6. I credited Jordan Morgan with 5 forced turnovers, and they were all individual efforts. A couple of them seemed very … professional (over-selling the amount of contact on a Thomas Robinson screen, and using his hip to subtly knock over an off-balance Kansas player and draw a travelling call). He’s a guy I would definitely want on my team if we’re both playing at the Y.

P.D.S.S. ELSEWHERE ON THE WEB

As I mentioned before, I found only a few references to Project Defensive Score Sheet online. Here’s the rundown, if you’re interested in reading more:

Kevin Broom of RealGM.com wrote an article for Sports Illustrated a little over 5 years ago.
A Toronto Raptors fan named Boris shows up in several different places. He collected these (or similar) numbers for Raptors games. Here are the season-long results for 2009-10.
Here are some PDSS stats for a few University of Pacific games from a few years ago.
It looks like a Duke fan was planning to keep track of these stats. I’m awaiting moderator approval so I can contact him and see if he followed through.
I read on a forum somewhere that 82games.com collected this type of data, but I haven’t found it. They do, however, have “counterpart” data (i.e. if Player X plays PG, how did the opposing team’s PG do while he was on the floor).
Here’s some discussion on the logistics of charting games.

I’ll at least track these stats for a few more games, simply because I enjoyed the alternative viewing experience of focusing on defensive details. If anybody has any suggestions on how to use the numbers (or insight into how useless they are - save me the effort!), please chime in.

Gone, Gaddy, Gone

2011-01-05T17:58:00.001-05:00

[EDIT: Ken Pomeroy posted a treatise on plus-minus which pointed out there’s a great deal of error inherent in the use of +/- data, something which has also been pointed out to me by John Ezekowitz. At this point, I’d advise you to take everything I wrote below with a huge grain of salt. And I doubt you’ll be seeing any more +/- analysis from me.]

About a month ago, I predicted that Duke would be fine without Kyrie Irving. My reasoning was that, in the games while Irving was still healthy, Duke had actually performed better with Irving on the bench than they had while he was on the floor – about 7 net points per 40 minutes better. Well, since the toe became The Toe, Duke has actually played ever so slightly better, with a post-Irving-injury adjusted efficiency margin of 39, compared to 37 with Irving:

Obviously that difference is insignificant, but the main point is that, so far, they’ve been fine.

I fear Washington’s prognosis for life without Abdul Gaddy is not so optimistic. The same +/- data that soothed Blue Devil worries paints an uglier picture for the Huskies (click to enlarge):

Whereas Duke was 7 points per 40 minutes better with Irving on the bench, Washington has been almost 7 points per 40 minutes better with Gaddy in the lineup. Some of the Twitter buzz hoped that the injury would give freshman CJ Wilcox a spot in the limelight. That’d be great, as he has a team-high 131.8 ORtg in his limited time. Taking a quick look at his +/- data, though, Washington has struggled with him in the game, despite his good offensive rating. A safe bet here would be that Wilcox’s defense is a step down from Gaddy’s, though the KenPom individual stats don’t paint a big defensive difference between the two, other than Wilcox’s more frequent fouling. Whatever the reason for the struggles, somebody will have to step up if Washington hopes to retain its lofty rating. If I’m Seth Davis, I’m changing my Washington grade from BUY to HOLD.

[On a side note, it’ll be a shame if this injury does end up impacting Washington, as I expect it to. The Huskies were looking like a great ratings litmus test, as they were in the top 5 in both Pomeroy and Sagarin’s PREDICTOR, yet #23 and unranked in the two major polls. Now watch, instead of tearing through the Pac-10 like their namesake through the Delaware, they’ll decline due to the impact of this injury, and the “you either win or lose” crowd will get a chance to trumpet their demise as a black mark against predictive ratings.]

All I Want For Christmas Is An Offensive Rebounder

2010-12-23T15:47:00.001-05:00

In the spirit of the season, I thought I’d dole out some gifts to 5 teams that have some glaring weaknesses.

PITTSBURGH

Pitt has the #1 offense in the nation (all ranks throughout the article are from the Pomeroy ratings), but ranks only 43rd in defense, which is a pretty big red flag, in terms of contending for a national title. Their main problems are a lack of turnovers (234th) and steals (246th), 3pt defense (223rd), and few blocks (114th).

Santa’s Bringing Them – Ayron Hardy, Jacksonville. The 6’5” senior was named A-Sun Defensive Player of the Year in 2008-09. He averages a steal every 20 opponent possessions (13th), blocks 4.7% of opponent shots (242nd), and helps anchor a Jacksonville defense that holds opponents to only 29.1% from deep (41st). The fact that he’s an extremely efficient role player on offense (130.2 ORtg, 16.7 %Poss) is a sweet bonus.

BAYLOR

Much like Michigan State, the Bears are fantastic on offense – when they don’t turn the ball over, which they do at an alarming rate (283rd in TO%). Their point guard, AJ Walton, has a decent assist rate (28.8%, 142nd), but more than counterbalances that with his turnovers (35.1 TO%), which contribute to his terrible offensive rating (93.8).

Santa’s Bringing Them – Nate Wolters, South Dakota State. The 6’3” sophomore is the only player in the country currently ranked in the top 50 nationally in both assist rate (36.4%, 29th) and turnover rate (9.2%, 46th). He also gets to the line at a decent rate (458th in FTRate), which is the next biggest offensive weakness for Baylor (99th in FTRate).

PURDUE

Obviously, what the Boilermakers really want for Christmas is Robbie Hummel back. That’s not going to happen, even in this little alternate universe, but maybe they can get the next best thing.

Santa’s Bringing Them – Marshall Moses, Oklahoma State. I tried to match a major conference player’s 2011 rate stats to Hummel’s 2010 line. Moses is the closest I could find to such a Hummelganger (Unless you have eagle eyes, you’ll need to click to see the figure, as it’s too wide to show here full size):

VILLANOVA

Villanova ranks #11 in defense, but only #34 in offense, and one of the team’s biggest problems is terrible outside shooting – their 31.5 3PFG% ranks 253rd.

Santa’s Bringing Them – this T-shirt, for Maalik Wayns and Corey Fisher. The duo has combined to shoot 20 of 90 (22%) so far from long range. If we assume their misses are rebounded by the Cats at a 40% rate (slightly higher than Nova’s season average OR%), that means they’ve used 68 possessions (20 makes + 48 un-rebounded misses) to score 60 points. Remove those points and possessions from the efficiency calculation, and their offense improves from #34 to #21. Granted, that’s not how it works in real life – some of those shots were probably taken because they couldn’t find a better option, so you can’t just not shoot. But still – STOP SHOOTING!

WASHINGTON STATE

The Cougars have a strong defense (#17). And on offense, they do a great job of holding onto the ball (22nd lowest TO%) and making shots (8th in eFG%). Yet they’re ranked only #48 in adjusted offense. Part of that is due to their easy schedule (269th in SOS), but part of it is due to anemic rebounding. They rebound only 30.5% of their misses, which ranks 244th nationally.

Santa’s Bringing Them – Kenneth Faried, Morehead State. The 6’8” senior’s 20.2 OR% is the highest in the country among players that play at least 60% of their team’s minutes (Fareid has played 80.9%), and he’s first in DR% as well, at 34.2%.

EVERY OTHER TEAM IN THE COUNTRY has a weakness or two as well, and if I were the real Santa, I’d have a gift for all of them: a healthy toe for Kyrie Irving and Duke, a trim and committed Renardo Sidney for Mississippi State, a continuation of Josh Selby’s blazing perimeter shooting for Kansas, somebody – anybody – to create some efficient offense for Florida State. But all I can leave them with is best wishes over the holiday break, for good health and good fortune in the coming year. Even for Missouri [replace with your favorite team’s rival, as necessary].

Mini-Preview: UNLV-Kansas State

2010-12-21T19:59:00.001-05:00

[EDIT: After writing this, Jacob Pullen and Curtis Kelly were ruled ineligible fro Kansas State. So, umm, never mind. KSU is no longer a “good” offense, so UNLV should be fine.]

[This post is really just an excuse to use a spreadsheet I whipped up, which takes a year and a school as input, and automatically creates a chart like the one below.]

UNLV tips off against Kansas State in Kansas City a mere hour from now. The Rebels started off the year with a bit of hype, and lived up to it at first, topping Pomeroy ratings #10 Wisconsin at home by 3, and blowing out some scrubs. But they’ve stumbled over the last 10 days, following up a very understandable loss at #13 Louisville with a much less forgivable home loss to #100 UC Santa Barbara. In truth, their problems started to show up 2 games previous to the Louisville loss, when they beat a poor Nevada (#202) team by only 12, but sometimes a W can have a lipstick-on-pig effect.

It’s clear that a 12-point win over #202 isn’t fantastic (Pomeroy had predicted UNLV by 18), but sometimes it’s hard to get a feel for exactly how good/bad a performance is. We can use opponent ratings to shed some light on the issue by using Pomeroy’s efficiency prediction formula:

Predicted Offensive Efficiency = ([Team Adj Off] + HFA) * ([Opp Adj Def] + HFA) / [Lg Avg Eff]

For each game that a team has played, we can replace the predicted efficiency with the team’s actual raw efficiency in that game, plug in their opponent’s rating, the league average rating, and the appropriate home field advantage (+/- 1.4% for each team, in a normal H/A situation) , and solve for [Team Adj Off]. That gives us the team’s single game adjusted offensive efficiency rating – essentially, this is how efficient a team would have been if they played exactly the same, but were facing an average opponent on a neutral court. We can do the same for defense, and from those two numbers we can calculate the efficiency margin (which I find more intuitive to use) or Pythagorean rating (which Pomeroy uses to rank teams). As a last step, we can take the single game Pythagorean rating, pretend that’s how the team has played the whole year, and see where they would rank in the Pomeroy ratings. That allows us to say, for example, UNLV played like the #232 team in their loss to UC Santa Barbara. The chart below shows this “played like” rank for each of UNLV’s games so far. (It also shows, from left to right, the location, opponent, opponent ratings, raw game efficiencies, and adjusted game efficiency ratings.)

You can see that UNLV played great through the first 7 games, and then has really struggled over the last 5. They’re still rated #22 by Pomeroy, but taking a close look at the individual game adjusted ratings reveals something interesting. Their overall defensive rating so far is 89.7, but that’s largely because of ridiculous defense in a few games against poor offensive teams. Here are the teams they’ve managed to post a sub-90 defensive rating against so far, along with those teams’ offensive ratings (keep in mind, average is 100):

UC Riverside (90.6)
SE Louisiana (91.7)
Illinois State (97.1)
Southern Utah (90.9)

That’s it. Their 30th ranked defensive rating comes in large part from really cranking the screws on the little guys. If they want to compete in the Mountain West this year, that’s going to have to change. And if they want to win their semi-road game tonight against Kansas State (offensive rating of 106.7), that’s going to have to change. I’m not saying it definitely won’t, but I’d say KSU has a better chance than UNLV of bettering their Pomeroy prediction (KSU by 1) tonight.

Bruce Pearl Before Swine

2010-12-15T19:38:00.001-05:00

As I’m sure anybody reading these words must know, Tennessee managed to achieve “fuhreal” status on Saturday, and fritter it away by Tuesday. It was an impressive about face, as they played like the #1 team in the country at Pittsburgh, then like UC-Irvine at home against Oakland:

A few messages insinuating that the loss showed Bruce Pearl’s true colors flashed across my Twitter feed last night, and it made me wonder if this kind of massive quality swing was really as rare as people were making it out to be. So I checked.

I found every instance since the 2002-03 season where a team:

Won on the road vs. a top 10 opponent (by Pomeroy rating)
In their next game, lost to a team ranked 50 or lower

I figured that would catch only very impressive wins. And I couldn’t really make the loss section much more strict, because Oakland is a pretty decent team (#71 as I write this). With the above criteria, there were 9 cases from the last 8 and a half years.

And, wow. A third of them are Bruce Pearl’s Tennessee teams. So for non-Tennessee teams, the chances of it happening any given year are around a quarter of a percent. For one of Pearl’s Tennessee teams, the chances are around 50%. Apparently there’s something to the idea that his teams are prone to a big letdown after a huge win. Of course, the fact they even have 3 road wins over top 10 teams is something to be proud of. I wish I would have kept track of how many teams didn’t lose their next game after a top-10 road win, so I could account for opportunities. But at this point I’m not going to go back through again. My eyes are already bleeding.

Interaction Effects and Diminishing Returns

2010-12-11T15:09:00.001-05:00

BACKGROUND

This continues what’s turned into an ongoing conversation between myself and Nathan Walker (aka @bbstats) of the basketball distribution, which started with my last post, continued over at Nathan’s site, and has been supplemented on Twitter. I started by trying to find out the effect that turnovers have had on the Michigan State offense, which I did by calculating what was essentially an opponent-adjusted version of what Nathan later more intuitively converted to:

[Pts/Possession] – [Pts/(Possessions – TO)]

This tells us how much a team’s offensive efficiency would change if their turnovers all magically disappeared. Turnovers are a very simple case: there can be only 1 or 0 turnovers on each possession; and when there is a turnover, a team never scores on that possession. Contrast that with rebounds: in theory, a team could gain 20 offensive rebounds in one possession, yet not score; another team could score after every single offensive rebound. The only way I could think of to track this kind of thing is to look at play-by-play data, which can get extremely time consuming, extremely quickly.

Nathan came up with another way of looking at the efficiency impact of rebounding and the other Four Factors, though. He published an Excel spreadsheet (in this post) that uses a regression equation to ask, for example, “What would Arizona’s predicted offensive efficiency be if we changed their eFG% to the league average value of 48.5%, and what’s the difference between that value and their actual efficiency?”

INTERACTION EFFECTS

This a pretty cool set up, in that you can quickly and easily see how each of a team’s Four Factors is contributing to their success. But because it uses a regression to come up with a single parameter for each factor, which is the same across all teams, we lose what I was trying to get at in my post about the Spartans – a turnover for MSU costs more points than a turnover for, say, Alcorn State, because even when the Braves hold onto the ball, they still average under a point per possession.

To try to address this, I used Nathan’s same idea, but I included interaction effects in my regression. That’s a statistics term which means that besides using eFG%, TO%, OReb%, and FTRate in my equation, I also used combined terms like [OReb% x TO%] or [eFG% x eFG%]. This lets me account for the fact that increasing your eFG% makes your TO% more important (because every turnover is now costing you more points). Stats nerds who want to see the details of the regression, including significance, can check this image to enlarge:

Only some interactions were statistically significant, so only some were included in the equation Here they are, along with what the sign (+/-) of the parameter says about the interaction:

eFG% x TO% … this makes sense – the better you shoot, the more you’re losing when you turn it over.
eFG% x FTRate … this one is negative – the better you shoot from the field, the less important it is for you to get to the FT line.
eFG% x eFG% … this one surprised me – it’s negative, meaning there are diminishing returns as you increase your eFG%. I’ve got no good explanation for this.
TO% x FTRate … again, the more free throws you shoot, the more points you’re losing when you commit a turnover.
TO% x OReb% … same story – a higher OR% means a better offense, which means you lose more when turn the ball over.
OReb% x OReb% … this one is positive, meaning there is an amplification effect - increasing your OReb% by 2% is more than twice as valuable as increasing it by 1%. I’m guessing because an offensive board often leads to another shot, which leads to another opportunity for a rebound.

Note that TO% is up there in combination with all 3 other factors. In fact, in one version of the equation, with some defensive stats included, TO% itself was not significant, but all the interactions were. This makes perfect sense to me, as a turnover’s value really is dependent on what a team does on the non-turnover possessions.]

IMPACT CALCULATION

OK, now, the reveal, right? Wait, one more thing. I’m also calculating the impact a different way. Instead of calculating Impact as

[team’s actual raw efficiency] – [team’s predicted efficiency when setting one factor to league average]

I’m calculating it as

[team’s predicted efficiency using correct four factors values] – [team’s predicted efficiency when setting one factor to league average]

The reason for this is that the predicted efficiencies when using the correct four factors values can be off by up to ~5.5 points. That error gets added into each factor’s impact when calculating it the first way, while it gets cancelled out in the second way. I’m listing the error in a 5th column in the spreadsheet. At first I was tempted to label it “Intangibles Impact,” but I imagine it’s mostly just a result of the random distribution of events in a game (e.g. whether a missed FT is the front end of a 1-on-1).

OK, NOW, FINALLY, THE REVEAL

Here’s a new version of Nathan’s Four Factor impact spreadsheet, using the new equation that contains the interaction effects, and using my alternate way of calculating Impact:

Excel file Four_Factors_Impact_v2.xlsx

For those of you who skipped down to this part, this shows how much of a team’s raw offensive efficiency is due to them excelling at or doing poorly in one of the Four Factors. Here’s a one-line example showing that Kansas’s great offense is almost entirely due to their high eFG%, with a bit of help from TO%. They’re pretty close to average in rebounding and getting to the free throw line:

Anyway, take a look, pick it apart, and leave some constructive criticism, because the only thing here I’m sure of is that it isn’t perfect.

How Much Are Turnovers Hurting Michigan State?

2010-12-10T18:49:00.001-05:00

[NOTE: Almost the exact same article can be written for Baylor, except that Baylor hasn’t played any tough games, and as a result are undefeated. But I wanted to choose just one team to refer to throughout. So, Baylor fans, just Ctrl+H and replace “Michigan State”/”Tom Izzo” with “Baylor”/”Scott Drew”.]

A 6-3 record against a tough schedule certainly isn’t the end of the world, and as The Only Colors pointed out, the Spartans have had plenty of success in the postseason after slow November/December starts. But Michigan State was ranked #2 in the preseason AP poll, and the team is clearly struggling more than expected. Taking a look at their stats page on kenpom.com, what jumps out are the big red splotches on the left: they’re ranked 322nd nationally in Turnover%, 293rd in FT%, and 298th in Steal%. But what are those marks costing Tom Izzo’s team? Quite a lot of offense, it turns out.

TURNOVERS/STEALS

One way to gauge the effect of turnovers is to look at what happens when a team doesn’t turn the ball over. To calculate a team’s offensive efficiency on possessions where they managed to hang on to the ball (TOAdjOff), I used a simple formula:

TOAdjOff = Adjusted Offensive Efficiency / (1 – Turnover%)

I then subtracted this from their actual adjusted offensive efficiency, to get what I’ll call turnover cost. It tells us how much a team’s adjusted offensive efficiency would increase if they somehow never turned it over. Here’s the top 20 in the country:

In case you’re wondering, that value of 150.6 for TOAdjOff is 3rd in the country, behind Duke and Georgetown. When the Spartans don’t turn it over, they’re among the best of the best.

Of course, a turnoverless team is a pipe dream; a more reasonable goal for the Spartans is to try to improve their TO% from abysmal to merely average. This seems doable – the average MSU TO% over the last 8 years has been 21.4%, which is right in line with this year’s national average of 21.2%. Using the same concept as above, but adjusting TO% to 21.2% instead of 0%, Michigan State ends up with an offensive efficiency of 118.7 (a gain of 6.5 over their current 112.2). That would bump their offensive rank from 26th to 4th, and their Pomeroy ranking from 14th to 5th. Couple that with what I can only assume would be a dip in opponent transition points, and they could rise even higher.

FREE THROWS

Michigan State is nearly as poor at free throw shooting as they are at preventing turnovers, but it’s not nearly as important because: A) a missed free throw only costs 1 point, while a wasted possession costs, as we saw above, 1.5 points; and B) there tends to be far fewer free throw attempts than possessions.

The Spartans have a 63.4 FT% so far, compared to a national average of 68.1%. Over their 202 FTA, that amounts to a difference of 9.6 total points. Working back from their number of possessions, that works out to 1.5 points per 100 possessions.

ALL TOGETHER NOW

If you add those 1.5 points onto the 6.5 gained from reducing turnovers, Michigan State’s offensive efficiency would rise to 120.2. However, because the gap between the top 4 teams (Duke, Kansas, Ohio State, and Pittsburgh) and the rest of the field is so large, their overall ranking wouldn’t change. Still, if Tom Izzo can tighten up (see also: tighten up) his leaky boat, he’ll have a good chance of floating down to Houston, come April.

How much will Kyrie Irving’s Absence affect Duke?

2010-12-09T23:31:00.001-05:00

There was a great post by the basketball distribution a few days ago about the effect of individual players on a team’s offense. It ended up being extremely topical, given A) his choice of Duke as one of the example teams, and B) Kyrie Irving’s toe. He calculated that removing Irving’s production from the Duke team would in theory reduce their offensive efficiency by about 3.1 points per 100 possessions, if the vacated minutes were filled proportionally by the other Duke players according to their share of minutes played so far, and if they all played at the same level. (Obviously, those assumptions are questionable, but it’s the best that can be done with the available data.)

I thought I’d take a look at it from a different angle, one that is probably on even more tenuous footing, but that is nonetheless interesting. I thought that perhaps, as a freshman, Irving might not be as advanced as his teammates defensively, so losing him might actually improve Duke’s defense. The only way I could think of to investigate this was to use StatSheet’s plus-minus data to find out what has actually happened this year when Irving sits on the bench. Before I get to the numbers, I should strongly emphasize that this data is very dependent on who all the other players in the game are, and we shouldn’t take it to seriously. But I thought the results were drastic enough, and surprising enough, that they’d be of interest.

What you see above in the “Irving plays” section are Kyrie Irving’s minutes, along with number of points that Duke has scored and given up when he’s been on the floor. The “Irving sits” obviously shows the same data for when he’s on the bench, and the last column shows the difference in net points per 40 minutes between Duke’s performance with Irving in the game and rving out of the game. Positive is good. Take a look at the totals at the bottom: no matter how you slice it (remove blowouts, remove cupcakes, exclude the last game that he missed entirely), the Blue Devils have a higher Net/40 margin when Irving is out of the game.

Now, I originally did this because I thought it might show that Irving hurts them on defense, but we actually see that teams score at a higher rate when he sits. However, I believe that’s largely due to a higher pace. Looking at the offensive numbers, the difference between 84 and 96 pts/40 is almost surely not entirely due to increased efficiency; there must be some tempo increase there. And if the tempo is faster, it’s pretty hard to say whether any of the increase in opponent scoring is due to worse defense. Either way, it’s outweighed by the increase in Duke’s scoring, and the Devils end up with a better scoring margin when Irving sits.

That doesn’t necessarily mean that the Blue Devils are better when he’s on the bench, though. It could also be that he happens to be sitting when the opponent’s best players are sitting, and the drop off from Irving to the Duke bench is less steep than the drop off from the starters to the bench of the opponent. In fact, there’s anecdotal evidence that this is true: Irving’s bench minutes coincided with Curtis Kelly’s in the game versus Kansas Sate; and he left the game just as Shelvin Mack cramped up in the Butler game. But the mere fact that Irving isn’t needed in order to take advantage of these missing players is a good sign for the Devils going forward.

On top of this, there’s even a reason why we might expect Duke to do better on offense without Irving. Andre Dawkins is the most efficient player on offense for the Blue Devils, and as we learned via Luke Winn’s pretty pictures, Nolan Smith seems to be much better than Irving at getting the ball to Dawkins.

The bottom line is that, while losing a player like Kyrie Irving is unlikely to be a positive for any team, this Duke team seems to be put together in a way that may allow them to overcome this obstacle. This isn’t a Purdue-losing-Hummel situation. It’s more like a Kansas-without-Shelby situation, and that seems to be working out just fine for the Jayhawks.

The Most Best On The Best [Or: Skillz]

2010-12-08T19:06:00.001-05:00

[Apologies to Mrs. Rowland, Mrs. Akers, Mrs. Davis, and all my other English teachers for the mangled title.]

I wanted to do something to celebrate this week’s early Christmas present, the unveiling of tempo-free player stats on kenpom.com, but it’s too early, and the schedules have been too sweet, to draw many meaningful conclusion. So I decided to do something kind of fun and interesting and statistics-oriented, but totally nonanalytic. Something Stark-ish. I went through Pomeroy’s top 25, and for each team, I found the one player who attained the highest ranking in one of the tempo-free stats. I excluded % of minutes played, offensive rating, % of possessions used, and % of shots taken, because I was looking for specific skills, not overall quality. For the most part, these turned out to not be the first player you think of when you think of a team, which is what I hoped. There are some players below who have some potential, and need to build around a skill they already have, some that clearly are on the court only to do that one particular thing, and some who are just great at everything.

[For definitions of the stats below, see “PLAYER SECTION” here.]

1. KANSAS – Markieff Morris, 36.6 DR% (1st)

The Lesser of the Morrii gets the spotlight here. His DR% last year was “only” 20.5% (152nd), after a 20.0% his freshman year, but both of those Kansas teams had Cole Aldrich in the middle, gobbling up opponent misses. It stood to reason that his numbers would go up this year, but not this much. They should drop back down to earth against Big XII opponents.

2. DUKE – Kyrie Irving, 69.6 TS% (36th)

Freshmen are not supposed to be this good. They’re supposed to force bad shots, settle for jump shots too easily, maybe not practice free throws enough. But Irving’s shooting percentages are 59%/45%/90% (2/3/FT), and he’s 147th in the country in drawing fouls. And to top it off, he’s 87th in assist rate, at 31.2%.

3. OHIO STATE – Dallas Lauderdale, 16.9 Blk% (3rd)

Only 8 teams in the entire country block shots at a higher rate than Lauderdale does when he’s on the court. And it’s a good thing he is so dominant, because the rest of team blocks basically nothing. He has 69% of tOSU’s blocks, despite only playing 10.4% of the player-minutes.

4. PITTSBURGH – Talib Zanna, 17.0 OR% (24th)

The 2010 season was the first year since Pomeroy started tracking OR% that Pitt didn’t have a monster in the middle (DeJuan Blair 09/08, Aaron Gray 07/06, Chevon Troutman AND Chris Taft 05). Though Zanna’s not at Blair’s level, he has helped propel the Panthers to 1st in the nation in team OR%. Cue Radiohead.

5. WASHINGTON – Aziz N’Diaye, 19.8 OR% (7th)

All the 7-foot Senegalese N’Diaye does is block shots, rebound, and miss free throws. Despite his impressive ranking in OR% and Blk% (35th), the number that practically leaps off the page when viewing his stat line is his 140.9 FTRate. If he had enough minutes to qualify, that would place him 2nd in the NCAA, after Memphis’s Wesley Witherspoon. Unfortunately, he only shoots 42% from the line. Maybe somebody who watches more UW ball can fill me in – is he getting to the line because it’s so easy for him to receive a pass in close, and teams are forced to play tough defense? Or is this the result of some Hack-A-Ziz defense? Seems like the latter could work out pretty well.

6. ARIZONA – Derrick Williams, 75.9 TS% (4th)

Was there any doubt it wouldn’t be Williams listed here? He’s also 6th in eFG%, 11th in OR%, 11th in FD/40, 47th in FTRate, has a shooting line of 65%/82%/79%, and is, in my book, the frontrunner for National POY. His only weaknesses are a low assist rate (but really, why should he pass it?) and a high foul rate (5.1 per 40 minutes). The latter has helped limit him to only 61.6% of possible minutes played, and his 5th foul in the Wildcats’ game against Kansas was the turning point.

7. ILLINOIS – Demetri McCamey, 42.7 ARate (12th)

Combined with his team-high 24.2 %Poss, this means that 66.9% of Illinois possessions (when he is on the floor) end with a McCamey shot, turnover, or assist. Having not seen an Illinois game yet, I’m curious what happens to the team when he sits. Do they drift, rudderless, in a sea of frustration?

8. KENTUCKY – Josh Harrellson, 17.5 OR% (21st)

Admittedly, Harrellson is a role player, using only 10.6% of possessions in his 57.9% of possible minutes. But given that Kentucky is only 146th in the country in 2PFG% (48.7%), it’s a pretty important role. (Outside of Harrellson, the next highest ranking for any UK player is 59th, for Terrence Jones’s 7.1 FD/40.)

9. WISCONSIN – Tim Jarmusz, 4.1 TORate (3rd)

Pomeroy notes that TORate “can be highly dependent on context. Players that do little passing or dribbling (i.e. spot-up shooters) will have an artificially deflated TO%.” Jarmusz has taken 27 shots this year. 24 of them are 3-pointers. He’s only made 7 of them (29%). Yuck. (The next highest ranking for a Badger is Jon Leuer’s 113th in Blk%.)

10. PURDUE – JaJuan Johnson, 6.0 TORate (19th)

In sharp contrast to Wisconsin’s Jarmusz, Johnson is most definitely not a spot-up shooter. Only 15 of his 116 FGAs have been 3’s, and he draws 5.8 fouls per 40min (244th). Johnson and E’Twaun Moore have put Purdue on their backs this year, with each having >25 %Poss and >28 %Shots, while still ranking 2nd and 3rd on the team in ORtg.

11. GEORGETOWN – Julian Vaughn, 13.0 Blk% (13th)

Vaughn’s Blk% has nearly doubled from last season’s 7.0%, and his share of possessions has risen from 19.3% to 25.4%. But that last increase might be bad for the Hoyas, as his ORtg is only 102.3 – only freshman Nate Lubick is lower, among players with %Min >20.

12. BAYLOR – A.J. Walton, 4.5 Stl% (67th)

This one isn’t quite fair, as LaceDarius Dunn would probably be listed for eFG%, TS%, or FD/40, if not for the fact he’s missed time due to suspension. Walton doesn’t contribute much value beyond his steals; he might subtract value, as he has an ORtg of 93.1 while using a significant number of possessions (22.7%).

13. UNLV – Chace Stanback, 67.3 eFG% (37th)

UNLV has a difficult-to-guard weapon in Stanback, a 6’8” junior who is tall enough to compete down low, but can step out and hit a three (11 for 29 through 8 games). The Rebels seem to know it – they let Stanback take the most shots when he’s on the flower (26.4%).

14. MICHIGAN ST – Draymond Green, 25.7 DR% (39th)

A poor man’s Derrick Williams, in terms of filling the tempo-free stats sheet: he ranks in the top 309 in everything except %Min, %Shots, TORate, and FC/40. It’s surprising that he’s the team’s best defensive rebounder, despite being only 6’6”, but what’s more surprising is that he shoots nearly identical from 2, 3, and the FT line: 56%/54%/55% on 45/24/38 attempts.

15. VILLANOVA – Maalik Wayns, 37.0 ARate (32nd)

Can you dish out a ton of assists, avoid turning it over too much, and still have a negative effect on your team’s offense? Apparently Wayns can. Eyeballing the numbers, it’s almost certainly due to his 5 for 30 performance from long range. Last year he only hit 32% on 54 attempts, so there’s no guarantee that will substantially improve.

16. BYU – Jackson Emery, 5.8 TORate (16th)

This was the battle of do-nothings, as Emery’s low TORate (the product of not doing much other than shooting on offense) edges out Jimmer Fredette’s extreme foul avoidance on defense (24th in FC/40 at 1.1). Another spot-up jumper type (shoots over 7 threes per game), Emery also contributes a 53rd-ranked 4.7 Stl% on the other end.

17. SYRACUSE – Scoop Jardine, 43.9 ARate (9th)

True story: I turned on Syracuse-MSU last night, and the very first play I saw was Scoop Jardine making a beautiful pass to Rick Jackson for an easy dunk. Guess I had about a 50/50 shot. He’s Jason Kidd-esque, but that unfortunately includes the terrible shooting range, as he’s 13 of 47 (28%) on threes this year.

18. WEST VIRGINIA – John Flowers, 9.4 Blk% (48th)

If he were an inch shorter, at 6’6”, he’d be the top shot blocker for his height. Unfortunately, the top swatter in the whole nation happens to be 6’7” William Mosley of Northwestern St, so Flowers is just another guy that’s good at defense. He’s also a solid rebounder, and gets to the FT line enough that he’s a net positive on offense.

19. TENNESSEE – Brian Williams, 18.0 OR% (15th)

He’s mostly your typical clumsy giant – good at rebounding and blocking shots, doesn’t get very involved in the offense (17.3 %Poss), and bricks a lot of FTs (58% on the year). But he’s cut his TORate by a third from last year, and ranks 292nd lowest.

20. SAN DIEGO ST – Kawhi Leonard, 26.9 DR% (24th)

The Aztecs’ MVP so far, Leonard’s 122.6 ORtg is 12th in the country among players with a usage rate of at least 26%. As a freshman last year, he was a high-usage (24.6%), low-efficiency (106.5) player, but two things have changed this year. He’s cut his turnovers in half, to 9.1% (79th), and he’s improved his 3PFG% from 21% to 38%. The rebounding was already good, and hasn’t dipped this year.

21. LOUISVILLE – Terrence Jennings, 15.9 Blk% (4th)

According to the height/weight numbers for last year and this year on kenpom.com, Jennings lost 1 inch and 20 pounds in the offseason. I'm not sure about the inch, but the getting rid of that 20 pounds seems to have given him some extra bounce – his block rate is up over 50%.

22. FLORIDA – Erving Walker, 1.4 FC/40 (64th)

Wow, this is a boring one. Walker gets plenty of steals (3.4%, 263rd), so it doesn’t seem like his low fouls are due to a swinging door defensive philosophy. Florida has the 4th lowest opponent FTRate in the country, so I’m going to give nearly all the credit to Florida’s use of zone defense.

23. VANDERBILT – Festus Ezeli, 21.3 OR% (2nd)

Festus Ezeli actually has a higher OR% than DR%, which is weird. Maybe Festus Ezili is too busy blocking shots (9.6%, 42nd) to worry about grabbing rebounds. Or maybe Festus Ezeli is getting his defensive boards stolen by equally-awesomely-named teammate Steve Tchiengang (pronounced ChainGang … at least when I say it). Festus Ezeli.

24. KANSAS STATE – Freddy Asprilla, 15.7 OR% (46th)

Hmm, Asprilla has the exact same rebounding rate on offense and defense. Maybe this is more common than I though. The fact that Asprilla has enough minutes to qualify is probably a bad sign for the Wildcats, as he has an ORtg of only 92.9. And commits 6.3 fouls per 40 minutes, oh dear. Which probably means Frank Martin would like him to play even more. This is funny: his 31% FT% is better than 3 other teammates.

25. TEXAS – Tristan Thompson, 108.8 FTRate (13th)

Getting to the line for more FTA than FGA is usually a good thing, since FT’s are usually the most efficient way to score. But not when you shoot 48% from the FT line, and 53% from the field. Thompson is just a freshman, so hopefully he will improve. He’s already a slight positive on offense, even with so many missed opportunities.

Hype Clouds

2010-11-16T21:55:00.001-05:00

I’m finding myself a little behind the curve this year, in terms of keeping abreast of this year’s college basketball story lines. I blame moving cross-country, the beautiful San Diego weather, and the fact that the Kansas City Chiefs still have playoff hopes in November.

Oh, sure, I’ve seen the big headlines:
“Turkish guy ineligible!”
“Bruce Pearl hosts sweet backyard BBQ!”
“Hummel reveals secret identity: Glass Joe!”

But I’ve somehow avoided the usual talking-up of new recruits, revelations that a player spent all summer in the gym, and “in-depth” conference previews that name drop one or two players from each team. As a result, I don’t know who I am supposed be sick of yet.

To remedy this problem, I thought it would be fun to (1) count how many times a player was name-dropped in various season previews, and (2) make a word cloud out of the results. Accomplishing #1 was more difficult than I thought, at least if I wanted to do it for more than one or two teams. So instead, I recorded the number of results found on Google News from October 17 to October 31, with a ton of assistance from Excel and URL Opener. [I chose October 31 to avoid most exhibition game recaps, and October 17 because Google wouldn’t let me go back any further. The player names I searched for were simply taken from ESPN.com’s team rosters.] Then I massaged the numbers a bit (so that end of the bench players would actually be visible), popped them into Wordle, and voila. Ladies and gentlemen, your Baylor Bears:

A couple of things to note:
1) Scott Drew’s name is ALL CAPS. I did that with all the coaches, to differentiate them slightly.
2) The absolute sizes of these names should not be compared to those on other teams. Each team’s cloud gets scaled so that it is roughly the same size, so the main thing to notice here is how big the biggest names are, compared to the others on the same team. It should give you a sense of how much of the hype surrounding a team is focused on a specific player or two (or a coach).

Wow, this is already educational for me: I had no idea who Perry Jones is. A quick glance at the Bears’ roster shows he is a really tall freshman.

Let’s do it for the rest of the 35 teams that I found in at least one major preseason Top 25 ranking. Clicking each thumbnail should take you to a higher resolution version, so you can read all the tiny names of the unheralded players. Hopefully you can figure out the teams based on the players and coaches shown, but if not, they’re in alphabetical order, and the images themselves are named after the team.

Finally, here is a combined cloud, made from all these 35 teams. This is the one you should use if you’re trying to compare the hype of players on different teams. (But, remember, hyped players on mediocre teams won’t be included here.)

Wow, that was time consuming, but fun. Hopefully at the end of the year, I’ll remember to revisit this post, and compare the preseason hype with how much the players actually contributed to their teams.

[NOTE: Feel free to re-use one or two of these, as long as you credit me by linking back to this post. But please don’t post the whole lot of them elsewhere. Thanks.]