Do Matchups Matter In College Basketball? (Stat Geek Idol 2 Finalist)

This post is one of the five finalists in our second Stat Geek Idol contest. It was conceived of and written by Jordan Sperber (@hoopvision68).


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.

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 (click the image to enlarge the tables):

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. (Again, click the image to enlarge.)

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.


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.