# NBA Fouls – Survival Analysis

This is Part 3 of my series on DeMarcus Cousins and how NBA players accrue personal fouls.
Part 2 can be found here.
Part 1 can be found here.
I strongly recommend reading at least Part 2 before continuing as I reference it.

Survival Analysis

To provide more statistical rigor, we analyze our players using a conditional risk set model for ordered events. This model, first proposed by Prentice, Williams, and Peterson, models the hazard at each foul event time as a function of the current number of fouls accumulated and time since the last foul. The model is flexible and can include other covariates as needed. For this paper, our covariates include the lead or deficit in the score of the player’s team, game time in minutes, and an interaction between the two. We chose these covariates, as we believe that a closer game can have an impact on a player’s fouling rates. We include actual game time in minutes to reflect how close the game is to ending, and to account for potential overtime periods.

Let $X_{ki}$ and $C_{ki}$ be the foul and censoring time for the kth foul (k=1, 2, …,6) in the ith game and let $Z_{ki}$ be the vector of covariates for the ith game and with respect to the kth foul. We assume $X_{ki}$ and $C_{ki}$ are independent given $Z_{ki}$. We then define $T_{ki}=\min(Z_{ki},C_{ki})$ and let $\beta$ be a vector of unknown regression coefficients.  Under the proportional hazard assumption, the hazard function of the ith game and for the kth foul is:

$\lambda_{k}(t,Z_{ki})=\lambda_{0}\left(t\right)e^{\beta Z_{ki}}$

From Table 2, we can see that the difference in score plays a minimal impact on player fouling rates, even after adjusting for game time for Cousins, Horford, and Lopez. Closer games do not seem to cause more fouls to be committed. However, the total game time that has been played has an impact. Furthermore, as time goes on, it appears that players are less likely to foul. This trend holds true for our three players of interest and all players when pooled together, which is surprising considering that players are more likely to foul later in the game. With this analysis, it shows that players are more likely to foul if they have already fouled as the game goes on. If a player has not fouled already in the game, they are less likely to foul since time plays a negative relationship with likelihood to foul. This trend holds true for all centers we analyzed. These results are line with what we saw in Figure 1. Moreover, these results are similarly likely due to the selection bias we have that precludes us from seeing every foul in every game.

As before, we can limit our analysis to games where the players had at least 5 fouls, and examine analysis of the first four fouls. Table 3 displays the survival model output for Cousins, Horford and Lopez when we use the restricted dataset. For all players, fouls 2, 3, and 4 are committed significantly sooner than the prior foul. To find the hazard ratios associated with each foul, we exponentiate the difference in the coefficients since each coefficient is with respect to the baseline of the 1st foul. For example, when Cousins has 3 fouls he is 405% more likely to commit a foul at any given time than when he only has 2 fouls. Cousins is 303% more likely to commit a foul when he has four fouls compared to when he only has three. Although the hazard ratios increase dramatically with each foul, it is important to keep in mind that the initial probability of fouling at any given moment is low, as the initial foul takes nearly 500 seconds (over 8 minutes) to take place on average for DeMarcus Cousins.

It is interesting to note that the opposite effect happens with game time. As each minute passes in the game, Cousins is only 90% as likely to commit a foul as the previous minute. This trend holds for all players.

From the table, we can see that although all players seem to have this “tilting” behavior, DeMarcus Cousins has a higher likelihood of committing a foul than other players as he accrues fouls. Cousins seems to “tilt” more than others centers in our analysis. Part of this behavior may be explained by teams attacking players who already have many fouls, attempting to get them in foul trouble. However, we believe that no one factor can tell the complete story.

Part 4 of this series can be found here