Across 52,521 one-home matches, home players beat their ELO expectation by 2.0 percentage points on the ATP side and 1.0 on the WTA side. Against the closing price the effect mostly disappears: the devigged residual is +1.41pp pooled and clears zero on neither tour alone. We filed it as rating calibration, not edge.
Tennis is a clean place to test home advantage: no home stadiums in the team-sport sense, a global calendar, and the same two players swapping the home label a few weeks later in another country.
Three findings. Home advantage is real: +2.0 percentage points against rating expectation for ATP players, +1.0 for WTA, on 52,521 one-home matches. Measured naively, it gets the wrong sign, because home wildcards pull the average down. Against the closing price, about half of it is already priced, and what remains is too thin to stake.
The published record is split. Koning modelled professional matches and found significant home advantage for men, while "the performance of women tennis players appears to be unaffected by home advantage" [1]. Wunderlich, Corten, Furley and Memmert studied German team tennis across levels and found home advantage independent of competition level, gender and COVID crowd restrictions [2]. Two careful papers disagreeing usually means a small effect that depends on sample and setting.
We grade against ELO expectation rather than raw win rates, because home players are not average players. ELO methods rank among the most accurate published tennis forecasters [3], and our blend is the measured optimum from our surface-ELO study.
Our database stores no venue country, so we derived it from tournament names with a reviewable 700-venue map: 91.7% of matches resolved to a host country, and the rest were excluded, never guessed. A match enters the sample when exactly one player is playing in their own country (52,521 matches). Both-home matches cancel by construction (16,248). The 101,563 neither-home matches are the control: the rating's miscalibration at each ELO gap is subtracted from the home cells at the same gap. Both players need at least 20 prior rated matches, for the reason covered below.
| cell | n | vs ELO, adjusted | 95% CI |
|---|---|---|---|
| ALL | 52,521 | +1.59pp | [+1.20, +1.98] |
| ATP | 33,387 | +1.97pp | [+1.47, +2.46] |
| WTA | 19,134 | +0.94pp | [+0.31, +1.58] |
Both tours are positive and significant, with disjoint confidence intervals: the ATP effect is roughly double the WTA effect. That reconciles the two papers. Our WTA main-tour cell alone is +0.38pp on 3,411 matches, not significant, the slice closest to what a tour-level study like Koning's could see [1]. The WTA headline is carried by volume at ITF level (+1.31pp on 14,067 matches), the lower-level territory where the German data found its effect [2].
| tier | n | vs ELO, adjusted | 95% CI |
|---|---|---|---|
| ATP main tour | 4,363 | +2.03pp | [+0.65, +3.40] |
| Challenger (M) | 14,158 | +1.77pp | [+1.00, +2.53] |
| ITF (M) | 14,866 | +2.14pp | [+1.41, +2.87] |
| WTA main tour | 3,411 | +0.38pp | [−1.12, +1.87] |
| WTA 125 | 1,656 | −0.97pp | [−3.15, +1.22] |
| ITF (W) | 14,067 | +1.31pp | [+0.56, +2.05] |
The ATP effect is flat across tiers, near +2pp at main tour, Challenger and ITF alike.
With no burn-in requirement the effect disappears: ATP reads −0.22pp and WTA −1.30pp. Home players appear to underperform.
The cause is wildcards. Tournaments give main-draw and qualifying spots to local players, so lightly rated locals land in the home cell. A player with a handful of rated matches still carries our tier debut prior (1250 at ITF, 1350 at Challenger, 1500 at tour level), which is not a measurement: the rating is too high on the day the wildcard puts them on court, and the losses fall in the home column. Sweeping the burn-in threshold, the measured advantage rises until about 20 prior rated matches and then goes flat, a data-quality boundary rather than a tuned setting.
The favourite/underdog split agrees. ATP home favourites and home underdogs both sit at +1.97pp adjusted. WTA home favourites get +2.16pp and WTA home underdogs +0.08pp, and the WTA underdog cell is where wildcard selection concentrates.
In the closed-crowd COVID window (March 2020 to June 2021), ATP home advantage did not vanish: +4.97pp [+1.11, +8.83] on 640 matches, above the +1.91pp of normal times, with a wide band. The WTA closed-crowd cell (196 matches) is too thin to read. The German team-tennis study reached the same conclusion from its own COVID window [2]. That leaves travel, sleep, familiar courts, food and scheduling as better candidates than the crowd.
Host-country cells scatter as small cells should, with a few clearing the mean: the United Kingdom (+4.68pp on 1,427 ATP matches), Australia for WTA players (+5.07pp on 1,020), the USA on both tours. We read those as small-sample spread around a real +2pp mean until a larger panel says otherwise.
In the window where we hold closing odds (February to July 2026), with exactly one home player and 20+ rated matches on both sides, we measured home players against our ELO and against the devigged market:
| tour | n | vs our ELO | vs market |
|---|---|---|---|
| ATP | 3,072 | +2.71pp [+1.10, +4.32] | +1.04pp [−0.54, +2.62] |
| WTA | 1,717 | +2.57pp [+0.42, +4.71] | +2.07pp [−0.02, +4.16] |
| ALL | 4,789 | +2.66pp [+1.37, +3.95] | +1.41pp [+0.15, +2.67] |
Against our rating the effect replicates out of sample. Against the market about half of it is gone: bookmakers already adjust their lines for home players. The residual +1.41pp clears zero pooled, barely, and clears nothing on either tour alone, on a five-month window. As with the style effects we tested, the reading is suggestive, unproven.
Nothing that stakes money. A new contextual signal must beat the closing price before it can touch a bet, and this one has not. Unlike the layoff curve, it is not shipped: it is queued as a rating-calibration variant (our ELO underestimates home players by about 2pp) with a no-edge annotation, so no analysis leads a betting thesis with it. We will recheck the priced residual when the odds panel is three times this size and publish the result either way.
Method: 302,945 country-resolvable matches, ATP and WTA, all tiers; 8,165 of 10,102 tournaments name-resolved to a host country (venues with no fixed country, ambiguous names and unmapped names dropped; 91.7% match coverage). Sample is matches with exactly one home player and 20+ prior rated matches for both players (n=52,521). Effect is observed minus ELO-expected home-player win rate; the adjusted column additionally subtracts the mean residual of neither-home control matches (n=101,563) in the same pre-match ELO-gap bin, removing the rating's own miscalibration at that gap. p-values are two-sided z-tests. The no-burn-in and burn-in-sweep numbers are from the 2026-08-12 run of the same command recorded in our research program memo; the market check uses the 2026-02-12 to 2026-07-17 odds slice with devigged closing prices, both sides 20+ rated matches, exactly one home player (n=4,789), measured 2026-08-12; the vs-ELO arm of it was re-verified 2026-08-14 on the same dates. All other cells are from the 2026-08-14 rerun.
References:
[1] R. H. Koning, "Home advantage in professional tennis," Journal of Sports Sciences, vol. 29, no. 1, pp. 19–27, 2011, doi:10.1080/02640414.2010.516762.
[2] F. Wunderlich, M. Corten, P. Furley, and D. Memmert, "Home advantage in tennis exists independent of competition level, gender and COVID-19 restrictions: evidence from German team tennis competitions," International Journal of Sport and Exercise Psychology, vol. 22, no. 7, 2024 (online 2023), doi:10.1080/1612197X.2023.2235592.
[3] 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.
Yes, and it is measurable: home players beat their rating expectation by +2.0 percentage points on the ATP side (33,387 matches) and +1.0 on the WTA side (19,134 matches), significant on both tours. The effect is roughly flat across main tour, Challenger and ITF levels for men. But it only appears once lightly-rated home wildcards are excluded; measured naively, wildcard selection flips the sign.
The crowd is not the load-bearing part. In the COVID closed-crowd window (March 2020 to June 2021) ATP home advantage did not disappear: +5.0pp on 640 matches, wide interval, but no collapse. Independent German team-tennis research found the same. Travel, sleep, court familiarity and scheduling are the better candidates.
Mostly no. Against the devigged closing price, home players came in at +1.41pp pooled on 4,789 matches in 2026, which barely clears zero and clears it on neither tour separately. The market already prices about half the effect. We queued it as a rating calibration with an explicit no-edge annotation, and nothing in our framework stakes on it.
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