Premier League Corner Intelligence
Sei stagioni di dati sugli eventi corner da tutte le partite della PL. Chasing index per squadra, corner attesi nel 2° tempo in base al punteggio all'intervallo e cosa il segmento di quote pre-partita dice sul volume dei corner.
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.
Profili Corner per Squadra
Ordinato per media corner guadagnati per partita. * = meno di 50 partite in casa (minor confidenza).
| # | Squadra | Casa | Trasferta | Totale | Conc.C | Conc.T | Chasing Idx | Tempo in svantaggio |
|---|---|---|---|---|---|---|---|---|
| 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. = corner subiti (C = in casa, T = in trasferta). Chasing Index = corner da in svantaggio ÷ corner da in vantaggio. Valori >2.0 = la squadra dipende molto dal chasing quando è sotto.
Chasing Index — Cosa significa per le scommesse
Queste squadre generano più corner quando recuperano. Evitate 'over corner totali' quando sono in vantaggio — il volume crolla.
Queste squadre attaccano costantemente indipendentemente dal punteggio. Il volume dei corner è prevedibile — sia i totali che gli handicap individuali.
Modello Live — Corner Attesi 2° Tempo per Punteggio all'Intervallo
Da usare all'intervallo: incrociate il punteggio HT (dal punto di vista della squadra di casa) con il segmento di quote pre-partita. Ogni cella mostra: corner totali 2° tempo / split casa | trasferta / campione. Scala colori: teal = basso, rosso = alto.
| Punteggio HT | Forte fav. casa (>62%) | Lieve fav. casa (50–62%) | Equilibrato (42–50%) | Lieve fav. trasferta (32–42%) | Forte fav. trasferta (<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 | — |
Come usare questo all'intervallo
- Annota il punteggio HT — es. 0-1 significa stato HT = HT -1 per la squadra di casa.
- Controlla le quote pre-partita — categorizza in strong_home / slight_home / balanced / slight_away / strong_away.
- Consulta la tabella — trova i corner totali attesi nel 2° tempo e il split casa/trasferta.
- Confronta con il mercato live — se il mercato offre Oltre 4,5 corner 2° tempo e il modello dice 6,0, 'over' ha valore.
- Controlla il chasing index — se la squadra in svantaggio ha un alto chasing index (>2,5), puntare ancora di più sui loro corner nel 2° tempo.
🛑 Effetto Cartellino Rosso sui Corner — Segnale Live
Basato su 135 eventi con cartellino rosso analizzati da 2.464 partite PL.
Effect by red card minute
| Minuto del rosso | Partite | Guadagno 11 gio./10min | Perdita 10 gio./10min | Impatto pratico (30min) |
|---|---|---|---|---|
| Presto ≤35' | 40 | +0.469 | −0.149 | ≈ +2,6 corner extra per la squadra a 11 in 30 min |
| Metà 36'–65' | 55 | +0.109 | −0.170 | ≈ +0,3 corner nei 15 min rimanenti |
| Tardi >65' | 40 | +0.339 | −0.276 | Maggior calo della squadra a 10 — passa alla difesa pura |
⚖ Possesso → Calibrazione Corner
4,735 team-match records. Pearson r = 0.465 (possession vs corners). Shots are an even better predictor: r = 0.544.
| Intervallo possesso | Partite | Corner medi | Visualizzazione |
|---|---|---|---|
| 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.
Efficienza Corner — Volume vs Pericolo
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.
| # | Squadra | C/Partita | Tiri/C | In Area | Colpi di testa | Testa in Porta | Punteggio Pericolo |
|---|---|---|---|---|---|---|---|
| 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
|
* meno di 50 presenze — trattare con cautela
👉 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.
Formazione Tattica vs Volume Corner
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.
| # | Formazione | Vinti | Sub. | 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 |
| # | Formazione | Vinti | Sub. | 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.
Come si Generano i Corner
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.
Miti sui Corner — Cosa Smentiscono i Dati
Three common bettor beliefs tested across 2,400+ matches. All three fail to reach even r=0.03 correlation with corner counts.
Metodologia
- 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.