Upset index
UEFA Europa League 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
- 5,090
- Upsets
- 1,474
- Upset rate
- 29.0%
- Upset index
- 139
Filters
Model data: 2,473,330 matches with a clear favourite between 26 Apr 1970 and 6 Nov 2026 (learning Elo, pre-match state).
Biggest upsets
By the winner's pre-match chance (Elo model) — lowest first
-
UEFA Europa League
Turun Palloseura1
Cercle Brugge2
Winner's pre-match chance 5.4%
-
UEFA Europa League
FK Partizan2
Toulouse3
Winner's pre-match chance 7.1%
-
UEFA Europa League · Round of 32
FC Porto0
Sevilla1
Winner's pre-match chance 7.3%
-
UEFA Europa League
AIK1
Shamrock Rovers1
Winner's pre-match chance 7.6%
-
UEFA Europa League
BSC Young Boys1
Athletic Club2
Winner's pre-match chance 7.7%
-
UEFA Europa League
FK Ventspils0
Hertha BSC1
Winner's pre-match chance 7.9%
-
Rosenborg BK
0–1
Bayer 04 Leverkusen
-
Club Brugge KV
2–1
Red Bull Salzburg
-
Rosenborg BK
0–1
FCSB
-
Molde FK
1–2
Zoria Luhansk
-
GNK Dinamo Zagreb
2–4
NEC Nijmegen
-
Basel
1–2
B-SAD
-
Helsingborg
1–3
Standard Liège
-
Airbus UK Broughton
1–3
NK Lokomotiva Zagreb
-
Liepajas Metalurgs
1–2
Prestatyn
-
1. FC Union Berlin
0–1
Royale Union Saint-Gilloise
-
NK Široki Brijeg
1–2
FC Kairat Almaty
-
FC Rustavi
2–5
Stade Rennais
-
Rosenborg BK
1–2
Atlético Madrid
-
Linfield
1–2
AO Xanthis
-
NK Maribor
1–2
Birmingham City
-
ETO FC Győr
0–1
Montpellier
-
ŠK Slovan Bratislava
1–2
Athletic Club
-
BK Häcken
0–3
AZ Alkmaar
-
Polonia Warszawa
0–1
NAC Breda
-
Shamrock Rovers
0–2
Juventus
-
SV Zulte Waregem
3–2
Lazio
-
FC Metalist Kharkiv
0–1
Newcastle United
-
FC Santa Coloma
0–1
NK Osijek
-
Panathinaikos FC
1–3
Standard Liège
-
University College Dublin
1–0
F91 Dudelange
-
Borussia Dortmund
0–1
PAOK
-
Sheriff Tiraspol
1–2
Olympique de Marseille
-
SK Slavia Praha
1–2
RSC Anderlecht
-
NK Celje
3–3
Sabah FK
-
Bodø/Glimt
1–2
Qarabağ FK
-
FC København
0–1
Girondins de Bordeaux
-
PAS Giannina
3–0
Odds BK
-
FK Crvena zvezda
1–2
Stade Rennais
-
Shakhtar Donetsk
0–2
Celta Vigo
UEFA Europa League: by season
The most chaotic and most predictable seasons
Giant killers
Win more than expected as the underdog
| Team | Underdog wins | Expected | Index |
|---|---|---|---|
 KRC Genk |
33/63 | 14.3 | 219 |
 PSV Eindhoven |
20/36 | 9.5 | 190 |
| Atlético Madrid |
12/20 | 4.7 | 188 |
| Bayer 04 Leverkusen |
11/19 | 4.3 | 184 |
| RSC Anderlecht |
22/51 | 11.2 | 184 |
| Standard Liège |
20/50 | 10.0 | 184 |
 Valencia |
13/22 | 5.6 | 184 |
| Royale Union Saint-Gilloise |
12/24 | 5.1 | 180 |
 Beşiktaş JK |
21/47 | 11.1 | 179 |
| Club Brugge KV |
21/51 | 11.1 | 178 |
Upset victims
Lose more than expected as the favourite
| Team | Losses as favourite | Expected | Index |
|---|---|---|---|
 UE Santa Coloma |
8/8 | 2.0 | 193 |
 HJK |
17/36 | 8.2 | 182 |
| NK Maribor |
12/24 | 5.2 | 179 |
| Rosenborg BK |
21/61 | 11.2 | 178 |
| Helsingborgs IF |
7/10 | 2.1 | 175 |
 SK Sturm Graz |
14/26 | 7.0 | 172 |
 Strømsgodset |
7/12 | 2.5 | 167 |
 Dinamo Minsk |
12/26 | 6.3 | 163 |
 FC Lusitanos |
5/5 | 1.3 | 162 |
 FK Austria Wien |
12/25 | 6.4 | 162 |
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.