Across 218,890 player-matches, next-day fatigue is a smooth gradient in the total games of the previous win, from a +3.4 point bonus after an easy win of 16 games or fewer to a -4.4 point penalty after 31 or more, on one day of rest. Our flat -4.5 point three-set rule was wrong in both directions.
The marathon story says a player who survives an epic is spent in the next round, and it comes with a number: a drop of roughly 14 percentage points in win probability.
We used a related rule ourselves. Our 209,000-match study found a three-set win the day before costs about 4.5 percentage points, and we applied that as a flat haircut in our betting framework. We re-measured on 218,890 player-matches, scoring outcomes against ELO expectation rather than raw win rates. There is no cliff, and the flat rule was wrong in both directions.
The 14-point figure comes from Jeff Sackmann's 2012 count of the 146 Grand Slam matches since 2001 whose deciding fifth set reached 6-6: the winners went 43-103 in the next round, a 29.5% win rate, against a rating-based forecast of 43.4% [1]. Five years later the same author bucketed by time on court in the previous match and found the ratio of actual to expected wins declining steadily from 1.07 under 90 minutes to 0.76 at 4:30 or longer, with no threshold anywhere [2]. The 2012 cliff is widely repeated; the 2017 gradient is not.
The physiology agrees. Gescheit and colleagues put trained players through four consecutive days of four-hour matches and measured progressive declines in sprint and jump capacity with elevated muscle damage markers, serve velocity preserved and accuracy degraded [3]. Reid and Duffield's review describes fatigue that builds within and between matches and worsens on consecutive days of play, with no discontinuity at a particular match length [4].
One observation is one player in one match, where the player won their previous match 1 to 14 days earlier (beyond 14 days is a layoff, a separate study). The outcome is a residual: result (1 if the player won, else 0) minus the pre-match ELO expectation, expected = 1 / (1 + 10^(−(elo_player − elo_opponent) / 400)), reported as the mean in percentage points. A raw win rate mostly measures that better players win; the residual asks whether load moves the result beyond what the rating knew. ELO-based methods are among the most accurate published tennis forecasts [5], so it is a fair bar.
Ratings are leak-free (stored post-match rating minus that match's change), on the pooled (all-surface) rating, used here as the yardstick. The model we serve blends 0.7 pooled with 0.3 surface, per our surface ELO study. A player who just won carries a rating bump from that win, so the baseline residual for any previous win is −0.13pp, close enough to zero to read the tables directly.
Sample: 218,890 player-matches across 125,432 matches, ATP and WTA, main tour through Challenger and ITF. Retirements are excluded by requiring that the previous match's winner closed out two sets (best-of-three) or three (best-of-five). Standard errors are clustered by match, because the two observations a match contributes are exactly anti-correlated.
| Games in the previous win | 1 day rest | 2 days | 3+ days |
|---|---|---|---|
| 16 or fewer | +3.42 (n=31,798) | +2.13 | +2.33 |
| 17-19 | +1.11 | +0.35 | −0.46 |
| 20-22 | −0.48 | +0.24 | −1.38 |
| 23-26 | −2.12 | −0.93 | −2.52 |
| 27-30 | −2.59 | −1.22 | −3.01 |
| 31 or more | −4.36 (n=15,445) | −2.36 | +1.46 |
Percentage points of win probability against ELO expectation. The bottom-right cell (+1.46) is not significant: read it as no measurable deficit, not as a rebound. Down the first column there is no step anywhere, from a bonus at the top to a penalty at the bottom, the shape the time-on-court study [2] and the physiology [3] imply. This replicates the 2017 result on a database roughly twenty times larger, down to Challenger and ITF, and adds the rest interaction and the easy-win bonus.
Total games also separates better than the binary three-set flag: 6.7pp from the lightest to the heaviest bucket, against 3.4pp for two sets versus three. In a regression on the residual, adding the games gradient and the rest interaction to the flat three-set rule is worth chi-square(5) = 249.7, p < 0.0001.
If marathons were different in kind, a deciding set that reached 6-6 should carry information beyond the game count. It does not:
| Previous win, deciding set | Residual |
|---|---|
| Reached 6-6 (tiebreak or extended set) | −3.60pp |
| Ended 12 games (7-5) | −3.68pp |
7-6 in the third and 7-5 in the third leave a player in the same shape the next day. Once continuous games are in the model, adding the 6-6 flag is worth chi-square(1) = 1.72, p = 0.19.
Our database is roughly 99% best-of-three: 573 ATP five-setters in 318,000 matches, and the best-of-five fifth-set 6-6 cell has n=92, too thin to report. We cannot test the exact population of the 2012 study [1]. In best-of-three professional tennis, the 6-6 flag is a slice of the games gradient and adds no independent effect, and no 14-point discontinuity appears in 218,890 observations.
| Rest before the next match | Fatigue slope |
|---|---|
| 1 day | −0.411 pp per extra game |
| 2 days | −0.226 pp per extra game |
| 3 or more days | −0.118 pp per extra game (not significant) |
Rest moderating load: chi-square(2) = 30.9, p < 0.0001. Game count is also a form signal, since a long win means the player only just got past that opponent. A form signal would not fade with rest days. This one halves after one extra day and disappears after two, consistent with physical recovery.
The strongest specification is the load differential against the opponent's own previous match: −0.523pp per game of differential, with 2.4 times the R-squared of the absolute version. A 30-game win matters less if the other player also played 30. ATP recovery is complete by three days, where the slope goes flat; WTA keeps a −0.364 slope even at three or more days of rest. We have no mechanism for the tour difference and treat the tours separately.
The previous rule was a flat −4.5pp for a three-set win the day before. First, it over-penalised the ordinary case: the three-set versus two-set gap on this sample is −3.36pp. The old estimate came from a binary split among day-after winners, opponent-adjusted and averaged per surface, on a smaller sample; as a continuous ELO residual with match-clustered errors, the same contrast is smaller. Median three-setters were haircut about a third too hard.
Second, it scored the largest cell in the study as zero. An easy win of 16 games or fewer the day before is +3.42pp on n=31,798, a bonus a flat penalty cannot express.
One artifact works against this result: our rating update applies a margin-of-victory multiplier of up to 1.5x on a blowout, so quick winners enter their next match on a more inflated rating. They beat it anyway.
The flat −4.5pp three-set haircut is deleted from our betting framework. It is replaced by a continuous games-by-rest term, applied to the load differential against the opponent's previous match where both are known, with the WTA slope kept at all rest levels. The tiebreak and marathon special case is deleted, since it measured a slice of the gradient twice. The earlier article stays up with a pointer to this one.
Check the gradient against closing prices, the only test that separates "our model was wrong" from "the market is wrong". Split the games slope by age, where recovery should differ. Investigate the WTA recovery gap directly instead of encoding it as a tour dummy.
Method: 218,890 player-matches across 125,432 ATP and WTA singles matches (main tour, Challenger and ITF), where the player won their previous match 1 to 14 days earlier. Outcome is the residual against the pre-match ELO expectation (stored post-match rating minus that match's change; all-surface basis). Load features parsed from set scores: total games, sets played, deciding set reached, deciding-set games, deciding set reached 6-6. Rest is calendar days between matches, capped at 14. Both players of an index match can qualify; when both sit in the same cell their residuals cancel, which is correct behaviour for equal load but reduces effective power below the raw n. Model comparison is OLS on the residual with CR0 cluster-robust covariance by match. Reproducible via the study command with --tour, --since and --json flags; database snapshot of 318,000 matches, run 2026-08-12.
References:
[1] J. Sackmann, "The hangover effect of a marathon fifth set," Heavy Topspin (Tennis Abstract blog), Aug. 9, 2012. http://www.tennisabstract.com/blog/2012/08/09/the-hangover-effect-of-a-marathon-fifth-set/ (accessed Aug. 12, 2026).
[2] J. Sackmann, "The negative impact of time of court," Heavy Topspin (Tennis Abstract blog), Jun. 2, 2017. https://www.tennisabstract.com/blog/2017/06/02/the-negative-impact-of-time-of-court/ (accessed Aug. 12, 2026).
[3] D. T. Gescheit, S. J. Cormack, M. Reid, and R. Duffield, "Consecutive days of prolonged tennis match play: performance, physical, and perceptual responses in trained players," International Journal of Sports Physiology and Performance, vol. 10, no. 7, pp. 913–920, 2015, doi: 10.1123/ijspp.2014-0329.
[4] M. Reid and R. Duffield, "The development of fatigue during match-play tennis," British Journal of Sports Medicine, vol. 48, suppl. 1, pp. i7–i11, 2014, doi: 10.1136/bjsports-2013-093196.
[5] S. A. Kovalchik, "Searching for the GOAT of tennis win prediction," Journal of Quantitative Analysis in Sports, vol. 12, no. 3, pp. 127–138, 2016, doi: 10.1515/jqas-2015-0059.
There is a hangover, but it is a smooth gradient, not a cliff. The more games a player needed to win their previous match, the worse they perform against rating expectation, from +3.42 percentage points after a win of 16 games or fewer to -4.36 after 31 or more on one day of rest. A deciding set that reached 6-6 (-3.60pp) costs the same as one that ended 7-5 (-3.68pp), so the marathon flag adds nothing once total games are counted.
The honest three-set versus two-set gap on 218,890 player-matches is -3.36 percentage points against ELO expectation, not the flat -4.5 we published earlier. And an easy win is not neutral: winning in 16 games or fewer the day before comes with a +3.4 point bonus, the largest single cell in the study (n=31,798), which a flat penalty rule scores as zero.
The rest decay is the evidence it is physical: the slope is -0.411 percentage points per extra game at one day of rest, -0.226 at two days, and a non-significant -0.118 at three or more. A pure form signal would not care how many days pass before the next match; this one roughly halves per extra rest day, which looks like recovery. It is evidence rather than proof, because our data has no match-duration column and total games also encodes how comfortably the player won.
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