In the continuing saga of exploring MSU’s struggles with turnovers, I put together a simple regression model to see if I could determine how significant the effect of pace is on MSU’s offensive turnover percentage. I looked at four potential independent variables–factors that may effect how often MSU turns the ball over:
- AWAY: Proxy for away games. Playing in hostile environments could cause a team to turn the ball over more. (Note: Texas coded as home game; Missouri/BYU coded as away; 0.5 for UCLA game.)
- DefTO%: Opponent’s defensive turnover % (for the season). The more turnovers an opponent tends to create, the more turnovers MSU is likely to make.
- DefEff: Opponent’s adjusted defensive efficiency rating. It could be that playing a team with a good defense overall–as opposed to a team good at creating turnovers but bad at other forcing tough shots and/or rebounding the ball–could cause MSU to struggle and turn the ball over.
- PACE: As asserted previously, my theory is that the slower the pace of a game, the more MSU bogs down in the half-court offense and turns the ball over.
The dependent variable is MSU’s offensive turnover percentage (MSUto%) for a particular game.
Data was pulled prior to this weekend’s games. Here’s the equation resulting from the linear regression I ran in Excel:
MSUto% = 33.05 + (-.064 x AWAY) + (1.14 x DefTO%) + (.07 x DefEff) + (-0.63 x PACE)
The R-squared for the equation is 0.44, so it explains a decent amount of the variation in MSU’s turnover percentage from game to game.
The results for AWAY and DefEff are (1) small coefficients, (2) not statistically significant, and (3) counterintuitive. Playing on the road appears to reduce MSU’s TO%, and they seem to turn the ball over more against defenses that give up more points per possessions. But the fact that neither of those two variables is statistically significant means we can’t really be sure of the sign of those coefficients. In short: let’s ignore them. (I ran the model without those two variables and it makes very little difference; I kept them in the model in the interests of full statistical disclosure.)
The other two variables are statistically significant:
- For every additional percentage point of the opponent’s defensive turnover %, MSU’s offensive turnover % goes up 1.14 percentage points. This coefficient is statistically significant at the .02 level. In other words, there’s a 98% chance the variable is having a positive impact on MSU’s turnover % (positive numerically, negative in terms of basketball performance).
- For every additional possession per game (pace), MSU’s turnover % goes down by 0.63 percentage points.This coefficient is statistically significant at the .05 level. In other words, there’s a 95% chance the variable is having a negative impact on MSU’s turnover % (positive in terms of basketball performance).
The first result is pretty intuitive for any basketball team: The more your opponent tends to create turnovers, the more you’re going to turn the ball over. This model indicates it’s basically a one-for-one relationship.
The second result is less intuitive. As discussed previously, generally pace and turnovers are correlated the other way (higher pace = more turnovers), since turning the ball over shortens possessions.
These results imply that if MSU plays a 70-possession game, rather than a 60-possession game, it will turn the ball over two fewer times. So MSU picks up 12 possessions of scoring opportunities, while its opponent only picks up about 8 possessions (assuming an average TO% of about 20% and a neutral relationship between pace and turnovers for the opponent). That’s 4 points per game, baby. (Update: I should have pointed out that it’s important to remember that pace is a combined function of both team’s playing styles in a particular game. So, even with a concerted effort, MSU may not be able to increase the pace of a game by 10 possessions.)
Now it’s much easier to create a statistical model than it is to get a basketball team to play at a faster pace in a competent manner. But any time statistical inquiry is consistent with intuitive judgement, I think it’s worth paying attention to.
So I’ll stick with my original “Eureka”-inducing assertion: MSU should push the ball more on offense and look to play a little more aggressively on defense to force the action. This may result in some errors on both ends of the floor, but it appears that playing more conservatively can result in errors, too–turnovers resulting from poor half-court execution.
For those of you with some statistical background, I’m interested in your comments/critiques.
For those you without statistical background, I hope I haven’t completely wasted two minutes of your life. And feel free to comment/critique, as well–but in a less geeky manner.