Do Matchups Matter?

The following is my entry for Stat Geek Idol. The contest is run by TeamRankings.com. Check out some of the entries from last year's competition here.

Introduction:

There are 347 teams in Division I college basketball. The nature of the sport allows for all different kinds of styles of play. Every team has varying player personnel and coaching philosophy. College basketball analysts are given the tough task of forecasting the end result of games featuring contradicting styles. It seems undeniable that, in some cases, certain teams can be bad matchups for other teams. Still, quite frequently analysts just say what sounds good. To illustrate this point, let’s look at a first round matchup from this year’s NCAA tournament:

The four factors (shooting, rebounding, taking care of the ball, and drawing fouls) are a very good way to assess style of play. The Minnesota-UCLA matchup featured the best offensive rebounding team in the country (Minnesota) and the 263rd defensive rebounding team in the country (UCLA). A smart analyst would point this compatible Minnesota strength and UCLA weakness out, but what does it really mean for the expected outcome of a game? On one hand, Minnesota should kill UCLA on the offensive boards, possibly creating a huge advantage for Minnesota. On the other hand, Minnesota kills just about everyone on the offensive boards. UCLA wouldn't be able to stop the lethal Minnesota rebounding attack regardless, so maybe this is a waste of an opponent weakness for Minnesota.

Essentially, the question I am asking here is simply: If you are really good at one of the four factors, would you rather play a team that is normally good at defending that factor (strength on strength) or really bad at defending that factor (strength on weakness)? At first thought, strength on weakness feels like the right choice. The goal of the following analysis is to try to answer this question.

Part 1: The effects of opponent on each four factor

To begin this study, I compiled a sample size of every single Division I college basketball game from 2009, 2010, 2011, and 2012. Games from 2013 (up until around the first week of March) were also included. I wound up with exactly 26,000 games to draw conclusions from.

In order to look at what happens when a {good/bad} offensive {eFG/TO/OR/FTR} team played a {good/bad} defensive {eFG/TO/OR/FTR} team, I had to define what exactly good or bad means. I decided that any team in the 90th percentile or better of a given four factor was “good” at that skill and any team in the 10th percentile or worse of a given four factor was “bad” at that skill.

NOTE: Good/bad is just the opposite for defense (i.e. - you want your opponent’s eFG% to be low)

The next step was to use these definitions of good and bad to find instances of strengths meeting strengths, weaknesses meeting weaknesses, and so on in the 26,000 game sample. First, let’s take a look at what happens when a good shooting team plays a good defensive shooting team:

The above table shows that there were 409 games where a good offensive eFG% team played a good defensive eFG% team. The offense averaged an eFG% of 54.5% on the season. However, when they played a good eFG% defense, that number decreased to 49.8%.

I did this same analysis for all types of matchups and all the four factors. The results are below:

There is a lot going on here, but the two biggest takeaways are:

1. Bad vs. bad brings out more good than good vs. good brings out bad. Basically, when two bad teams at one factor play each other, the offense improves a lot. When two good teams at one factor play each other, the offense does not diminish quite as much.

2. The defense controls FTR the most and eFG% the least. If you look at the percent change column, an offenses ability to get to the foul line changed a lot depending on the defense. On the other hand, an offenses ability to make shots did not change nearly as much. This is consistent with past research on similar topics.

Part 2: The effects of style on efficiency

Part 1 showed exactly what happens to the individual four factor based on opponent, but that is only so helpful in determining if there is evidence for good/bad matchups. The more important thing to look at is the effects of style on points per possession. Let’s go back to UCLA-Minnesota. Say UCLA decided that they needed to make an extra effort to keep Minnesota off the offensive glass. This decision might come at the risk of a different four factor. Maybe UCLA focusing on defensive rebounding diminishes their ability to create turnovers. This idea wouldn’t show up in the part 1 results, but it would show up in points per possession.

To look at the effects of efficiency, I first calculated an expected points per possession using simply the ORtg (adjusted for schedule) of the offense and the DRtg (adjusted for schedule) of the defense. This expected PPP was made without looking at matchups or style of play at all. Then, the expected PPP could be compared to the actual PPP. If the two numbers significantly differ, that means that mismatches in four factors can give us more information on which team will most likely win the game.

As you can see, matchups had virtually no effect on the actual points per possession of the game. I was able to predict PPP by simply using the offensive and defensive averages extremely effectively. Here are the final key takeaways from the tables above.

1. Four factor matchups don’t increase prediction accuracy. If we once again go back to Minnesota-UCLA, this means that we shouldn’t have looked too far into the offensive rebounding advantage. Simply looking at which team is better efficiency wise is adequate.

2. FTRate had very little effect on the points per possession of an offense. If you look at the Actual PPP column, there is not much change in general. This particular study indicates the eFG% is the most important four factor, followed by OR%, TO%, and finally FTR.

Conclusion

It would be foolish to say that specific matchups have no effect on the outcome of a basketball game. It doesn't mean that matchups can’t possibly matter just because this study shows no evidence for it. However, the study does indicate that it may not be wise to focus too much on the compatibility of the strengths and weaknesses of opponents. Trying to breakdown strength and weaknesses may be a futile activity. Simply put, the best way to predict the winner of a game appears to be just picking the better of the two teams.

Shabazz's Rebounding

Shabazz Muhammad's season ended on Friday at the hands of Minnesota. The Bruins had two big things working against them: the injury to Jordan Adams and the task of keeping Minnesota off the boards. The Golden Gophers are first in the country in offensive rebound percentage, grabbing 44% of their missed shots. On the other hand, UCLA has struggled all season long with rebounding. They rank 206th in the country in offensive rebounding and 264th in the country in defensive rebounding. At 6'6", Shabazz's personal OReb% was a solid 9.9%, but his DReb% was an awful 8.5%. To put that into perspective, Gonzaga's 5'11" David Stockton's was higher this season (9.2%).

Trying to keep Minnesota off the offensive glass had to have been a main priority for Ben Howland on Friday. The Bruins as a whole actually did a pretty good job of this. Minnesota had 10 offensive rebounds (32.4% OReb%, well below their season average). Still, if you look at the box score you will see that Shabazz had ZERO defensive rebounds in 39 minutes. That's actually pretty hard to do, you would think a ball is going to fall in your lap at some point in the game. 

How does an athletic 6'6" forward not get a single defensive rebound in 39 minutes? Well to answer that question I decided to go back and look at the film. Of Minnesota's 10 offensive rebounds, Shabazz was on the court for nine of them. The photos below highlight where Shabazz was on the court when Minnesota got their nine rebounds.

1. In good position but flat footed, spectating

For Shabazz's sake, I was hoping the reason for his lack of rebounding was to leak out in transition. Although this might not be an optimal strategy, it would mean there is at least an advantage to his lack of rebounding. Otherwise, Shabazz is simply very bad at the skills involved in rebounding or not hustling (or both). This first attempt shows a flat footed Shabazz under the hoop. In his defense, he has three UCLA teammates in the paint with him.

2. Boxing out

The problem with this analysis is we can't be sure of Howland's gameplan. Basketball purists are quick to teach the box out, but the fact is that's much easier said than done against Minnesota. Still, I think it's safe to say that Shabazz did a pretty good job on this possession

3. Late getting back in transition

Here Minnesota got an easy put back in transition off of Shabazz's miss on the other end. He was very late getting back, but we're not too concerned about this one in terms of rebounding ability.

4. Sort of boxing out the shooter

Shabazz was guarding the initial shooter on this one. He didn't box out immediately, but at least tried to recover after he realized the ball wasn't going in.

5. Flat footed, spectating

The ball doesn't go to Shabazz's side, but he is still pretty much just standing and watching the play unfold. This is an example of the worst scenario for Shabazz. There are obviously very few times in a basketball game where standing and watching isn't a bad thing.

6. Elevating for the rebound 

Shabazz gets his hand on this ball, but was not able to come up with it. The ball got batted out for a long rebound. By getting his hand on the ball, he may have prevented  an easy layup for Minnesota (even though they were still able to maintain possession).

7. Flat footed, spectating

Another negative play for Shabazz. He's not boxing out or going after the ball. He's not even leaking out, he's just standing and watching.

8. In good position but flat footed, spectating

More of the same here.

9. A bit more of pursuit

Shabazz took steps towards the basket to potentially grab a rebound, but the ball bounced off the rim hard. You can see in the photo he is changing his momentum to either try to continue to pursue the ball or possibly to leak out.

Obviously, this analysis is only nine plays from one game and is by no means definitive. I'll let the film speak for itself and not over generalize, but Shabazz's rebounding (and motor) might be a long term concern for his NBA career. Statistically, his rebounding numbers are somewhat similar to DeMar DeRozan. DeRozan was a pretty good offensive rebounder and a not so good defensive rebounder, but not quite as bad as Shabazz.

The Debates: Best Player

The following is part two of three of a series of debates featuring Jonathan Safir, Ricky Winkeller, and Jordan Sperber. For part two, we will be examining the best player in the KenPom player era (2005-2012).