Upset index

LaLiga upset index

Including the years before odds: the biggest upsets in history, the most chaotic seasons and the giant killers by our learning Elo model's pre-match probabilities. Index 100 = as many upsets as expected.

Matches with a clear favourite: 15,924
Upsets: 2,894
Upset rate: 18.2%
Upset index: 99

League

Season

Period

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Model data: 2,482,439 matches with a clear favourite between 26 Apr 1970 and 8 Nov 2026 (learning Elo, pre-match state).

Biggest upsets

By the winner's pre-match chance (Elo model) — lowest first

LaLiga: by season

The most chaotic and most predictable seasons

Giant killers

Win more than expected as the underdog

Giant killers
Team	Underdog wins	Expected	Index
Real Madrid	59/195	50.1	133
FC Barcelona	57/215	54.6	119
Getafe	84/373	81.5	118
Atlético Madrid	105/442	104.0	116
Girona FC	23/102	22.7	114
Rayo Vallecano	92/460	93.7	113
Villarreal	63/276	64.1	113
Lleida	3/15	2.5	111
Deportivo Alavés	59/289	61.3	111
Levante UD	63/307	65.7	110

Upset victims

Lose more than expected as the favourite

Upset victims
Team	Losses as favourite	Expected	Index
Rayo Vallecano	64/211	52.6	137
Albacete Balompié	18/62	14.7	130
Girona FC	20/68	16.5	130
Lleida	2/3	0.7	124
Eibar	17/58	14.8	124
Leganés	10/32	8.5	122
Huesca	2/4	1.1	118
Cádiz	30/110	28.6	118
Deportivo Alavés	34/129	32.6	118
Real Valladolid	84/349	85.4	113

How is it calculated?

Probability: From pre-match 1X2 (or two-way) odds, with the bookmaker margin removed proportionally: p = (1/odds) / Σ(1/odds). The same formula as the stats hub's “upsets by odds” block.
Clear favourite and upset: Only matches with at least a 10-point gap between the home and away win probabilities count; if the favourite's opponent wins, it is an upset. Draws are not upsets. Winner's chance of 25% or less = big upset.
Ranking: The lower the winner's pre-match chance, the bigger the upset (surprise score = 100 × (1 − chance)).
Index: 100 × (actual upsets + 5) / (expected upsets + 5); expected = sum of the underdogs' win probabilities. The +5 pulls small samples towards 100. Teams have two directions: giant killing (winning as the underdog) and fragility (losing as the favourite).
Calibration: Expected values are scaled by the source's overall hit factor (0.86 in this view), so a systematic bias of the source does not shift every league the same way.
Historical (model): For past matches without odds, the probability comes from our learning Elo model replaying the full history chronologically and taking its state before the match (both teams need at least 15 matches; friendlies excluded). Never mixed with odds.

LaLiga upset index

Filters

Biggest upsets

LaLiga: by season

Giant killers

Upset victims