Premier League — Intelligence Corners
Six saisons de données sur les corners dans tous les matchs de la PL. Indice de chasing par équipe, corners attendus en 2e mi-temps selon le score à la mi-temps et ce que le segment de cotes pré-match révèle sur le volume de corners.
The chasing effect is real but asymmetric. Total 2nd-half corners stay nearly constant (~5.9) regardless of HT score. What changes dramatically is the split: when the home team trails at HT, they earn 3.56 H2 corners vs 2.84 when leading. The trailing team is systematically underpriced on individual corner handicap markets.
Profils Corners par Équipe
Trié par nombre moyen de corners gagnés par match. * = moins de 50 matchs à domicile (confiance réduite).
| # | Équipe | Dom. | Ext. | Total | Conc.D | Conc.E | Chasing Idx | Temps en retard |
|---|---|---|---|---|---|---|---|---|
| 1 | Manchester City | 7.9 | 5.9 | 6.9 | 2.4 | 3.9 | 0.42x |
17% trailing
|
| 2 | Liverpool | 7.3 | 6.3 | 6.8 | 3.6 | 3.8 | 0.60x |
20% trailing
|
| 3 | Arsenal | 6.7 | 5.2 | 6.0 | 3.7 | 4.4 | 0.47x |
15% trailing
|
| 4 | Chelsea | 6.5 | 5.3 | 5.9 | 4.0 | 4.3 | 0.79x |
22% trailing
|
| 5 | Aston Villa | 6.0 | 5.1 | 5.5 | 4.6 | 5.7 | 1.46x |
32% trailing
|
| 6 | Bournemouth | 5.7 | 5.1 | 5.4 | 5.5 | 6.0 | 1.65x |
35% trailing
|
| 7 | Tottenham Hotspur | 5.9 | 4.9 | 5.4 | 4.4 | 6.0 | 1.29x |
31% trailing
|
| 8 | Luton Town * | 6.6 | 4.1 | 5.4 | 5.1 | 6.8 | 4.30x |
57% trailing
|
| 9 | Manchester United | 6.0 | 4.6 | 5.3 | 4.4 | 6.0 | 1.05x |
25% trailing
|
| 10 | Brighton and Hove Albion | 6.1 | 4.4 | 5.2 | 4.1 | 5.4 | 1.49x |
28% trailing
|
| 11 | Leeds United | 5.8 | 4.6 | 5.2 | 4.4 | 5.4 | 1.95x |
34% trailing
|
| 12 | Fulham | 5.4 | 4.7 | 5.0 | 5.0 | 5.2 | 1.54x |
28% trailing
|
| 13 | Newcastle United | 5.6 | 4.5 | 5.0 | 5.0 | 5.8 | 1.02x |
23% trailing
|
| 14 | Southampton | 4.7 | 5.2 | 4.9 | 5.6 | 5.6 | 2.26x |
42% trailing
|
| 15 | Leicester City | 4.9 | 4.6 | 4.7 | 5.1 | 5.8 | 1.77x |
37% trailing
|
| 16 | West Ham United | 5.0 | 4.5 | 4.7 | 5.0 | 6.2 | 1.78x |
33% trailing
|
| 17 | Brentford | 4.8 | 4.4 | 4.6 | 5.2 | 6.3 | 1.46x |
31% trailing
|
| 18 | Sheffield United | 5.3 | 3.9 | 4.6 | 5.5 | 7.4 | 3.59x |
41% trailing
|
| 19 | Everton | 5.1 | 4.1 | 4.6 | 5.2 | 6.2 | 1.65x |
29% trailing
|
| 20 | Burnley | 5.0 | 4.0 | 4.5 | 5.6 | 7.3 | 2.32x |
38% trailing
|
| 21 | Crystal Palace | 5.0 | 4.0 | 4.5 | 4.8 | 5.7 | 1.44x |
28% trailing
|
| 22 | Wolverhampton Wanderers | 4.7 | 4.2 | 4.4 | 4.9 | 5.8 | 2.58x |
36% trailing
|
| 23 | Watford * | 4.4 | 4.3 | 4.3 | 5.3 | 6.1 | 3.40x |
39% trailing
|
| 24 | Norwich City * | 5.0 | 3.6 | 4.3 | 6.6 | 7.1 | 3.51x |
42% trailing
|
| 25 | Nottingham Forest | 4.4 | 3.7 | 4.0 | 5.5 | 6.4 | 1.52x |
31% trailing
|
| 26 | West Bromwich Albion * | 5.7 | 2.3 | 4.0 | 6.2 | 7.7 | 1.28x |
25% trailing
|
| 27 | Ipswich Town * | 3.8 | 3.5 | 3.7 | 5.4 | 7.4 | 4.00x |
44% trailing
|
| 28 | Sunderland * | 3.8 | 3.3 | 3.6 | 4.8 | 5.8 | 2.33x |
27% trailing
|
Conc. = corners encaissés (D = à domicile, E = à l'extérieur). Chasing Index = corners en retard ÷ corners en tête. Valeurs >2,0 = l'équipe dépend fortement du chasing quand elle est menée.
Chasing Index — Ce que ça signifie pour les paris
Ces équipes génèrent le plus de corners en remontant. Évitez 'plus de corners totaux' quand elles mènent — le volume s'effondre.
Ces équipes attaquent constamment quel que soit le score. Le volume de corners est prévisible — totaux et handicaps individuels.
Modèle Live — Corners Attendus 2e Mi-Temps selon Score à la Mi-Temps
À utiliser à la mi-temps: croisez le score HT (du point de vue de l'équipe à domicile) avec le segment de cotes pré-match. Chaque cellule montre: total corners 2e MT / split dom. | ext. / taille échantillon. Échelle: teal = faible, rouge = élevé.
| Score MT | Fort fav. dom. (>62%) | Léger fav. dom. (50–62%) | Équilibré (42–50%) | Léger fav. ext. (32–42%) | Fort fav. ext. (<32%) |
|---|---|---|---|---|---|
| HT -2 | — | — | — |
6.5
4.0 | 2.5 n=19 |
5.4
2.8 | 2.6 n=42 |
| HT -1 |
6.8
4.8 | 1.9 n=20 |
6.8
4.8 | 2.0 n=35 |
6.0
4.4 | 1.6 n=32 |
6.0
3.3 | 2.3 n=58 |
5.4
2.7 | 2.5 n=98 |
| HT 0-0 |
6.2
4.2 | 1.9 n=96 |
5.6
3.4 | 2.2 n=90 |
5.6
2.9 | 2.7 n=71 |
5.8
3.0 | 2.8 n=111 |
6.0
2.5 | 3.4 n=160 |
| HT +1 |
5.6
3.5 | 2.1 n=90 |
5.8
2.8 | 3.0 n=65 |
5.4
2.1 | 3.3 n=46 |
5.6
2.5 | 3.1 n=45 |
6.1
1.8 | 4.0 n=59 |
| HT +2 |
5.4
3.6 | 1.8 n=35 | — | — |
6.7
2.4 | 4.2 n=21 | — |
Comment utiliser ça à la mi-temps
- Notez le score à la mi-temps — ex. 0-1 signifie état HT = HT -1 pour l'équipe à domicile.
- Vérifiez les cotes pré-match — catégoriser en strong_home / slight_home / balanced / slight_away / strong_away.
- Consultez le tableau — trouvez le total de corners attendu en 2e MT et le split dom./ext.
- Comparez avec le marché live — si le marché propose Plus de 4,5 corners 2e MT et le modèle dit 6,0, 'plus' a de la valeur.
- Vérifiez le chasing index — si l'équipe menée a un chasing index élevé (>2,5), miser encore plus sur leurs corners en 2e MT.
🛑 Effet du Carton Rouge sur les Corners — Signal Live
Basé sur 135 événements carton rouge avec fenêtres pré/post valides sur 2.464 matchs PL.
Effect by red card minute
| Moment du carton rouge | Matchs | Gain 11-hom./10min | Perte 10-hom./10min | Impact pratique (30min) |
|---|---|---|---|---|
| Tôt ≤35' | 40 | +0.469 | −0.149 | ≈ +2,6 corners supplémentaires pour l'équipe à 11 sur 30 min |
| Milieu 36'–65' | 55 | +0.109 | −0.170 | ≈ +0,3 corners sur 15 min restantes |
| Tard >65' | 40 | +0.339 | −0.276 | Plus forte chute à 10 — l'équipe passe en défense pure |
⚖ Possession → Calibration Corners
4,735 team-match records. Pearson r = 0.465 (possession vs corners). Shots are an even better predictor: r = 0.544.
| Tranche de possession | Matchs | Corners moy. | Visuel |
|---|---|---|---|
| 25–30% | 246 | 2.93 | |
| 30–35% | 342 | 3.58 | |
| 35–40% | 478 | 4.00 | |
| 40–45% | 595 | 4.44 | |
| 45–50% | 670 | 4.89 | |
| 50–55% | 665 | 5.47 | |
| 55–60% | 606 | 5.89 | |
| 60–65% | 490 | 6.50 | |
| 65–70% | 361 | 7.29 | |
| 70–75% | 282 | 8.35 |
ⓘ Key insight: shots (r=0.544) beat possession (r=0.465) as a corner predictor. Teams that generate shots — not just passes — earn more corners. Use expected shots (from xG models) as a better pre-match corner estimator.
Efficacité des Corners — Volume vs Danger
Winning more corners doesn't mean scoring from them. Manchester City average 7.05 corners/match — the highest in the dataset — yet rank last (17.8/100) on danger score. Their corners rarely reach the six-yard box (only 11.3% into-box delivery rate). Nottingham Forest average just 4.27/match but convert nearly every corner into a genuine scoring chance: 46.7% produce a direct shot, 53.1% generate a header.
| # | Équipe | C/Match | Tirs/C | Dans la Surface | Têtes | Tête Cadrée | Score Danger |
|---|---|---|---|---|---|---|---|
| 1 | Nottingham Forest | 4.3 | 46.7% | 39.2% | 53.1% | 11.4% |
88.8
|
| 2 | Sunderland * | 3.5 | 43.0% | 39.0% | 55.0% | 15.0% |
84.8
|
| 3 | Everton | 4.6 | 44.7% | 31.4% | 51.3% | 12.8% |
75.2
|
| 4 | Ipswich Town * | 3.9 | 45.5% | 30.3% | 46.2% | 11.7% |
70.6
|
| 5 | Brentford | 4.7 | 43.0% | 24.1% | 56.6% | 15.6% |
70.1
|
| 6 | West Ham United | 4.9 | 43.1% | 27.0% | 50.2% | 12.7% |
65.3
|
| 7 | Crystal Palace | 4.7 | 42.8% | 35.4% | 41.7% | 9.8% |
63.9
|
| 8 | Fulham | 5.1 | 46.0% | 23.6% | 47.5% | 10.0% |
63.6
|
| 9 | Leeds United | 5.3 | 44.4% | 29.4% | 42.5% | 9.9% |
62.0
|
| 10 | Norwich City | 4.5 | 39.6% | 42.7% | 35.1% | 9.1% |
58.0
|
| 11 | Newcastle United | 5.5 | 42.9% | 25.9% | 44.3% | 10.6% |
56.5
|
| 12 | Burnley | 4.5 | 38.7% | 31.1% | 47.4% | 11.5% |
55.4
|
| 13 | Luton Town * | 5.4 | 39.2% | 25.0% | 52.0% | 10.8% |
52.8
|
| 14 | Wolverhampton Wanderers | 4.6 | 39.3% | 27.3% | 43.2% | 11.4% |
49.1
|
| 15 | West Bromwich Albion * | 4.1 | 34.5% | 42.3% | 35.9% | 8.5% |
44.9
|
| 16 | Sheffield United | 4.7 | 34.9% | 40.1% | 35.3% | 10.0% |
44.6
|
| 17 | Southampton | 5.1 | 40.7% | 23.9% | 37.5% | 11.6% |
44.4
|
| 18 | Brighton and Hove Albion | 5.4 | 41.1% | 21.0% | 41.8% | 10.1% |
44.1
|
| 19 | Manchester United | 5.3 | 41.4% | 23.1% | 38.9% | 9.2% |
43.8
|
| 20 | Tottenham Hotspur | 5.6 | 38.2% | 26.2% | 36.3% | 9.3% |
37.4
|
| 21 | Bournemouth | 5.5 | 37.6% | 26.2% | 39.3% | 8.1% |
37.0
|
| 22 | Liverpool | 6.9 | 43.5% | 12.4% | 39.9% | 8.2% |
36.9
|
| 23 | Watford | 4.4 | 34.2% | 30.3% | 38.8% | 7.6% |
32.2
|
| 24 | Aston Villa | 5.7 | 37.7% | 23.4% | 35.5% | 7.5% |
30.4
|
| 25 | Arsenal | 6.0 | 41.2% | 17.2% | 34.2% | 6.3% |
29.9
|
| 26 | Chelsea | 6.1 | 37.1% | 16.2% | 37.2% | 8.5% |
23.4
|
| 27 | Leicester City | 5.0 | 32.6% | 28.8% | 32.0% | 6.4% |
19.8
|
| 28 | Manchester City | 7.0 | 39.3% | 11.3% | 31.8% | 7.4% |
17.8
|
* moins de 50 apparitions — à traiter avec prudence
👉 Betting angle: when a low-danger team (City, Arsenal, Chelsea) is generating corner volume against a physical side, the corners-on-target market may be overpriced. When Brentford, West Ham, or Forest earn even 4 corners, each one carries twice the headed-shot threat of a City corner.
Formation Tactique vs Volume de Corners
Formation is the single most actionable pre-match corner predictor. You know the formation before kick-off. A 4-3-3 playing at home averages 6.45 corners — nearly double the 5-4-1's 3.41. Wide formations (4-3-3, 4-2-3-1, 4-1-4-1) generate +0.97 more home corners than defensive/narrow setups (5-4-1, 5-3-2, 4-4-1-1). The effect is consistent: it holds both home and away.
| # | Formation | Gagnés | Conc. | Net | N |
|---|---|---|---|---|---|
| 1 | 4-3-3 | 6.45 | 4.14 | +2.31 | 496 |
| 2 | 4-1-4-1 | 5.98 | 4.46 | +1.52 | 113 |
| 3 | 4-2-3-1 | 5.65 | 4.66 | +0.99 | 897 |
| 4 | 4-4-2 | 5.40 | 5.32 | +0.08 | 232 |
| 5 | 3-4-1-2 | 5.38 | 4.42 | +0.96 | 40 |
| 6 | 3-4-2-1 | 5.35 | 4.41 | +0.94 | 210 |
| 7 | 4-4-1-1 | 5.12 | 5.41 | -0.29 | 59 |
| 8 | 3-5-2 | 5.09 | 5.09 | 0.00 | 120 |
| 9 | 3-4-3 | 5.01 | 4.40 | +0.61 | 88 |
| 10 | 4-5-1 | 4.93 | 6.17 | -1.24 | 30 |
| 11 | 5-3-2 | 4.87 | 4.97 | -0.10 | 38 |
| 12 | 5-4-1 | 3.41 | 6.63 | -3.22 | 46 |
| # | Formation | Gagnés | Conc. | Net | N |
|---|---|---|---|---|---|
| 1 | 4-3-3 | 5.41 | 4.84 | +0.57 | 476 |
| 2 | 3-4-1-2 | 5.05 | 6.05 | -1.00 | 43 |
| 3 | 3-4-3 | 4.99 | 5.59 | -0.60 | 81 |
| 4 | 4-2-3-1 | 4.75 | 5.45 | -0.70 | 836 |
| 5 | 4-4-2 | 4.70 | 6.08 | -1.38 | 233 |
| 6 | 4-1-4-1 | 4.48 | 6.06 | -1.58 | 111 |
| 7 | 3-5-2 | 4.26 | 6.56 | -2.30 | 135 |
| 8 | 3-4-2-1 | 4.13 | 5.55 | -1.42 | 243 |
| 9 | 4-4-1-1 | 3.92 | 6.78 | -2.86 | 63 |
| 10 | 5-3-2 | 3.65 | 7.63 | -3.98 | 54 |
| 11 | 5-4-1 | 3.28 | 7.07 | -3.79 | 58 |
👉 How to use: check both teams' confirmed formations before betting corners. A 4-3-3 vs 3-4-2-1 matchup averages 9.3 corners — the lowest matchup type. A 4-1-4-1 vs 4-3-3 matchup averages 12.3. That's a 3-corner swing driven purely by shape, before you even consider team quality or odds.
Comment les Corners se Génèrent
Corners come almost entirely from two sources: goalkeeper saves that are parried wide, and outfield blocks that deflect the ball out of play. Quantifying these conversion rates across all 2,464 PL matches reveals the mechanical link between shot volume and corner volume.
Team variation: the rate at which a goalkeeper's saves produce corners varies between 15.7% (Sunderland) and 30.0% (Southampton). Teams with high-quality goalkeepers who catch or hold shots (rather than parrying) concede fewer corners from saves — Chelsea (22.5%), Wolves (22.1%), Aston Villa (22.0%). Teams with shot-stopping GKs who push wide concede more — Southampton (30.0%), Nottingham Forest (29.3%), Everton (28.8%).
ⓘ Implication: teams that generate shots on target — not just corner-forcing set pieces — are the primary corner generators. Expected shots on target (xSOT) is a better predictor of corner volume than pass count or even possession.
Mythes sur les Corners — Ce que les Données Démentent
Three common bettor beliefs tested across 2,400+ matches. All three fail to reach even r=0.03 correlation with corner counts.
Méthodologie
- Source: premierleague.com commentary JSON — 2,464 matches, GW1–GW38, seasons 2019–20 through 2024–25.
- 25,674 corner events extracted and tagged with score state at moment of corner.
- Odds segments computed from normalised implied probability (bookmaker margin removed).
- Chasing Index = (corners earned while trailing) ÷ (corners earned while leading). Neutral = 1.0.
- Live model cells with n<10 are hidden (insufficient sample).
- Backtest (2024–25 season, n=291): MAE = 2.57 corners — consistent with the natural variance of corner distributions.
- Red card analysis: 135 events with valid 30-min pre/post windows. Corner rates computed per 10 minutes, normalised for window length.
- Possession calibration: 4,735 team-match records from clean_stats.json. Pearson r computed over all records. Possession brackets at 5% intervals.
- Corner efficiency: team_stats.json fields wonCorners, attCorner, attHdTotal, attHdTarget aggregated per team across all 2,464 matches. accurateCornersIntobox read from defending team entry (Opta stores it as opponent's in-box deliveries). Danger score = 0.35×shots + 0.35×into-box + 0.20×headers + 0.10×headers-OT, normalised 0–100.
- Formations: lineups.json formation field extracted for 2,461 matches. Min 30 appearances per formation for table inclusion. Wide formations defined as 4-3-3, 4-2-3-1, 4-1-4-1, 4-4-2, 3-4-3.
- Save→corner rate: 14,486 'attempt saved' events tracked for next-event corner within same minute. Block→corner: 18,260 blocked shots. Save-to-corner rate per team computed from defending team perspective.
- Weather: weather_summary.json provides temperature, precipitation (mm), wind speed (km/h) at kickoff for 2,329 matches. Pearson correlations computed against total match corners. Result: r<0.03 for all weather variables — not significant.
- Yellow card effect: 6,368 yellow card windows (±20 min) from 9,011 yellow card events. Corner rate computed pre/post per 10 minutes. Net swing +0.028/10min = 6% of red card effect.
- Rest days: advanced_metrics.json context.home_rest_days/away_rest_days tested against corners across 2,424 matches. Pearson r<0.02 for all rest day variables — not significant.