Some Analysis of Game-to-Game Fatigue

Fatigue is a factor in NBA scheduling, and fatigue leads to a higher risk or injury. This is not a revelation by any means; however, there still seems to be some resistance to the value of rest despite many peer reviewed research projects on the topic. In my hope to add to this academic collection. This past semester, I was given the opportunity to devote some type for a deeper look at the effects of fatigue on the NBA game.

We know from said previous research that days of rest between games and traveling time zones (particularly from east to west) are significant factors in determining the outcome of games. We also know that teams are now investing in a diverse selection of fatigue measuring technologies, ranging from Catapult GPS trackers on players to watches that track sleep to full body scanners searching for hidden stresses in a player’s posture. We know that the NBA is attempting to build in additional rest through an extended mid-season All-Star break while some teams[1. Most famously the Spurs, who just recently held out 30 year-old LaMarcus Aldridge on the second night of a back-to-back] are opting to rest crucial players during regular season games in hopes of strengthening their post-season prospects as well as the long-term of their players[2. You can find more of this in the Lit Review section of the paper].

However, what I could not find is empirical research on the specific and significant effects of fatigue on game play, so, naturally, I tried to answer this question. I first started by trying to confirm the significance of rest in the outcome of games over the history of the NBA, and my results mirrored that of previous research: Teams with more rest win more often when adjusted for team strength and location[3. It is amazing to modern sensibilities how often teams played back-to-backs in the 1960s and 1970s. Almost every other road game was a back-to-back!].

Then, using box-score statistics and SportVU-based metrics from the 2014-15 season, I conducted means testing looking for significant differences between games played of 0 days of rest and games played on 1+ days of rest. The following bullet points sum up the results:

• Offensive rating drops from 103.4 to 102.3 and defensive rating increases from 102.7 to 104.3 in zero rest games compared to one-plus rest games. This accounts for a net rating change from 0.6 to -2.0.
• Team field goal percentage decreases significantly from 44.5% to 44.2%. This is accompanied by 2.21 fewer points in the paint.
• Teams average almost one less possession per game in zero rest games. Given the slower pace, it naturally follows fast break points per game fall from about 13.4 to 12.3.
• Ball movement is significantly affected. There are 0.78 fewer assists and 0.3 fewer secondary assists, with overall assist ratio falling about .5%. This is despite an insignificant difference in passes and touches between the two types of games, which suggests a hidden effect on player movement.
• Defense is affected too. Points off turnovers fall from 16.4 to 15.5, steals drop from 7.8 to 7.5, and blocks decrease from 4.9 to 4.5.

(The full means testing results are available the paper’s appendix on pages 32-35)

Interestingly, teams pass at the same rate regardless of rest, yet it is the quality of pass that suffers as represented by the decrease in assists and secondary assists. Quality passing requires split second decision making, so more fatigue seems to slow this reaction time. Similar reaction statistics like steals and blocks also significantly decrease.

I had already investigated in the drop in points and shooting percentage here and here, so I decided to take a more empirical approach in this paper to extend on this research. Using Krishna Narsu’s KOBE metric (against using 2014-15 SportVU data), I added in factors for rest. We know that player take more difficult shots on the second of back-to-backs, but I wanted to see whether fatigue is negatively affecting shot making in itself. Simply, does fatigue make players miss shots they usually make. Conventional wisdom would suggest that tired leagues lead to missed jumpers.

In contrast to my initial hypothesis, my investigation found fatigue was not a significant factor. I split up shots into six buckets based on two levels. First, the lineup position of the player (as estimated by height) and the type of shot (at the basket, 5-10 feet, jumper). In none of these buckets were days of rest a significant factor in overall accuracy of shooting. Thus, if shot-making doesn’t drop off, the decline in observed efficiency is a result of worse shot selection due to fatigue rather than simple shot making.

So what does this all mean? To be honest, I am not really sure right now. This research perhaps suggests that teams who rely more on the three point shot are less affected offensively, but I have yet to look into this thoroughly. At the least, this identifies the areas of the game most affected and can allow teams to game plan around such disadvantages.

If you would like to see the paper in full as well as the step-by-step empirical analysis, the paper is embedded below.

The NBA and Fatigue by Adam Yudelman