A Weekly Model Predicts Jump Readiness Across a Basketball Season

Coaches want to know whether a player is fresh or fatigued before a hard week. A study of NCAA Division 1 women’s basketball players shows that a simple weekly test, the maximum vertical jump, can be predicted from data teams already collect. The model explained 71 percent of the week-to-week variation in jump height across a full season.

The research was conducted by Kevin W. McCurdy, Professor in the Department of Health and Human Performance at Texas State University, and Rasim M. Musal, Associate Professor of Information Systems and Analytics in the McCoy College of Business. It was published in the International Journal of Strength and Conditioning.

The study

Twelve players who were cleared for full participation took part. The researchers tracked them through the entire official season, from preseason through the in-season schedule.

Every practice and game was measured for workload, meaning the total amount of external work, and work intensity, meaning how hard that work was per unit of time. The two were multiplied to form a work density score. The team also recorded total volume-load from resistance training and asked players to rate their sleep quality, stress, and recovery.

Each Monday, players performed a maximum vertical jump. The researchers then asked which measures from the previous seven days best predicted that jump. The model also included the prior week’s jump, the week of the season, and whether the week fell in preseason or in-season.

What the researchers found

The combined model was statistically significant and accounted for 71 percent of the variation in weekly maximum jump. Every variable was a significant predictor except sleep quality and recovery.

The pattern of effects offers practical guidance. Lower total workload and lower lifting volume in the prior week tended to raise the next jump. So did higher work intensity and, somewhat surprisingly, higher self-reported stress.

Timing mattered within the week. A high work density score six days before the test, achieved by combining substantial workload with high intensity, had a positive effect. Three days before the test, high workload paired with low intensity was indicated. The day before the test, lower scores were warranted.

After controlling for everything else, jump height tended to decline across the in-season. The authors read this as a sign of accumulating fatigue. When a player’s jump drops, the following week’s external load should come down to allow recovery.

What it means for coaches and analytics staff

Many programs already collect workload data from wearable sensors, log resistance training volume, and run short wellness surveys. This study shows those streams can be combined into a single readiness forecast rather than reviewed one at a time.

Three things stand out. First, total volume and intensity pull in different directions, so a week can be made lighter in volume without becoming easy. Second, the calendar of the week matters. Placing the densest session several days out and tapering into the test is consistent with the results. Third, a weekly jump test serves as a check on the whole system. A falling number is an early warning that the load plan needs to change.

The sample is small and comes from one team in one season, so the exact coefficients should not be copied to another program. The approach, however, is general. Any team with consistent load tracking can fit a similar model and tune it to its own players.

This summary is based on the paper’s abstract. The full article reports the data, methods, and detailed results.

What it means for managers

• Track load week by week. Lower total workload and lifting volume in the prior seven days, paired with higher work intensity, went with higher jumps.
• Time the hard days. A dense, high-intensity session six days before a jump test helped. The day before the test, load should be light.
• Watch for drift. Jump height tended to fall across the in-season even after controlling for other factors, so a drop is a signal to reduce external load the following week.

McCurdy, K. W., & Musal, R. M. (2026). Prediction of maximal vertical jumps during the entire season in NCAA Division 1 women basketball players. International Journal of Strength and Conditioning, 6(1). 10.47206/ijsc.v6i1.509