Premier League Corner Intelligence
Sechs Saisons Corner-Daten aus allen PL-Spielen. Team-Chasing-Index, erwartete Ecken in der 2. Halbzeit nach Halbzeitstand und was das Quoten-Segment vor dem Spiel über das Eckball-Volumen verrät.
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.
Team-Eckball-Profile
Sortiert nach durchschnittlich gewonnenen Ecken pro Spiel. * = weniger als 50 Heimspiele (geringere Konfidenz).
| # | Team | Heim | Ausw. | Gesamt | Konz.H | Konz.A | Chasing Idx | Zeit im Rückstand |
|---|---|---|---|---|---|---|---|---|
| 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
|
Konz. = kassierte Ecken (Heim = zuhause, Ausw. = auswärts). Chasing Index = Ecken bei Rückstand ÷ Ecken bei Führung. Werte >2.0 = Team setzt stark auf Eckball-Chasing bei Rückstand.
Chasing Index — Was es für Wetten bedeutet
Diese Teams gewinnen die meisten Ecken wenn sie aufholen. Meidet 'über Gesamtecken' wenn sie führen — das Volumen bricht ein.
Diese Teams greifen unabhängig vom Spielstand konstant an. Das Eckball-Volumen ist vorhersehbar — sowohl Gesamtecken als auch Einzel-Handicaps.
Live-Modell — Erwartete Ecken 2. Halbzeit nach Halbzeitstand
Bei Halbzeit verwenden: HT-Stand (aus Heimteam-Perspektive) mit dem Pre-Match-Quoten-Segment kreuzen. Jede Zelle zeigt: Gesamt-Ecken 2. HZ / Heim | Auswärts-Split / Stichprobengröße. Farbskala: türkis = niedrig, rot = hoch.
| Halbzeitstand | Starker Heimfavorit (>62%) | Leichter Heimfavorit (50–62%) | Ausgeglichen (42–50%) | Leichter Auswärtsfavorit (32–42%) | Starker Auswärtsfavorit (<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 | — |
Wie man das zur Halbzeit nutzt
- Notiere den Halbzeitstand — z.B. 0-1 bedeutet HT-Status = HT -1 für das Heimteam.
- Pre-Match-Quoten prüfen — einteilen in strong_home / slight_home / balanced / slight_away / strong_away.
- Tabelle nachschlagen — erwartete Gesamt-Ecken 2. HZ und Heim/Auswärts-Split finden.
- Mit dem Live-Markt vergleichen — bietet der Markt Über 4,5 Ecken 2. HZ und das Modell sagt 6,0, ist 'Über' Value.
- Chasing-Index prüfen — hat das zurückliegende Team einen hohen Chasing-Index (>2,5), noch stärker auf ihre Ecken in der 2. HZ setzen.
🛑 Rote-Karte-Effekt auf Ecken — Live-Signal
Basierend auf 135 Rote-Karte-Ereignissen mit gültigen Vor-/Nachfenstern in 2.464 PL-Spielen.
Effect by red card minute
| Zeitpunkt Rote Karte | Spiele | 11-Mann Gewinn/10Min. | 10-Mann Verlust/10Min. | Praktische Auswirkung (30Min.) |
|---|---|---|---|---|
| Früh ≤35' | 40 | +0.469 | −0.149 | ≈ +2,6 extra Ecken für das 11-Mann-Team über 30 Min. |
| Mitte 36'–65' | 55 | +0.109 | −0.170 | ≈ +0,3 Ecken über 15 verbleibende Min. |
| Spät >65' | 40 | +0.339 | −0.276 | Stärkster 10-Mann-Abfall — Team wechselt in reine Defensive |
⚖ Ballbesitz → Ecken-Kalibrierung
4,735 team-match records. Pearson r = 0.465 (possession vs corners). Shots are an even better predictor: r = 0.544.
| Ballbesitz-Intervall | Spiele | Ø Ecken | Visualisierung |
|---|---|---|---|
| 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.
Ecken-Effizienz — Volumen vs. Gefahr
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.
| # | Team | E/Spiel | Schüsse/E | In den Box | Kopfbälle | Kopfball aufs Tor | Gefahrenwert |
|---|---|---|---|---|---|---|---|
| 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
|
* weniger als 50 Auftritte — mit Vorsicht behandeln
👉 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.
Taktische Formation vs. Eckball-Volumen
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 | Gew. | Kass. | 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 | Gew. | Kass. | 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.
Wie Ecken entstehen
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.
Ecken-Mythen — Was die Daten widerlegen
Three common bettor beliefs tested across 2,400+ matches. All three fail to reach even r=0.03 correlation with corner counts.
Methodik
- 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.