IPL 2026 AI Player Impact Index
Who actually wins matches — measured by win probability, not vanity stats
Updated: · 1 match analysed · Data: CricketPrediction AI Model
After 1 IPL 2026 match, Jacob Duffy (RCB) leads our AI Player Impact Index with a match impact score of 9.2/10 — his bowling shifted Royal Challengers Bengaluru's win probability by 22.5%. This page tracks every player's real match impact — not runs or wickets, but how much they actually changed the outcome. Updated after every match.
$5,000,000 Prize Pool
Sweet Holiday Chase · 15% rakeback with code maximus88
160% Welcome Bonus up to ₹16,000
Asia's most trusted sportsbook · Best cricket odds · Fast INR withdrawals
How the AI Player Impact Index Works
Win Probability Per Ball
Our AI model calculates win probability at every delivery. Each run, wicket, and fielding action shifts the number.
Contribution = Shift
A player's impact is the total win probability they shifted. A 30 off 12 in a tight chase is worth more than 80 in a dead game.
Beyond Stats
We measure batting, bowling, AND fielding impact. A game-changing catch counts. Orange Cap and Purple Cap miss this entirely.
Overall Impact Leaderboard
| # | Player | Impact ↓ | Win% Shift ↕ | ||||||
|---|---|---|---|---|---|---|---|---|---|
| 1 |  Jacob Duffy (RCB) | 9.2 /10 | +22.5% | ||||||
| 💡 3/22 in 4 overs — dismantled SRH top order in powerplay, removing Head, Abhishek, and NKR | |||||||||
| 2 | Devdutt Padikkal (RCB) | 8.7 /10 | +19.8% | ||||||
| 💡 61 off 26 balls (SR 234.62) — powerplay aggression put RCB ahead of required rate from over 3 | |||||||||
| 3 | Virat Kohli (RCB) | 8.4 /10 | +18.2% | ||||||
| 💡 69* off 38 balls — anchored the chase and accelerated through middle overs with 5 sixes | |||||||||
| 4 |  Ishan Kishan (SRH) | 7.8 /10 | +15.6% | ||||||
| 💡 80 off 38 balls (SR 210.52) — rescued SRH from early collapse with 97-run stand with Klaasen | |||||||||
| 5 | Rajat Patidar (RCB) | 7.1 /10 | +14.3% | ||||||
| 💡 31 off 12 balls (SR 258.33) — finishing cameo sealed the game in the 16th over | |||||||||
| 6 | Philip Salt (RCB) | 6.5 /10 | +12.1% | ||||||
| 💡 Two stunning boundary catches broke the Kishan-Klaasen partnership — game-changing fielding | |||||||||
| 7 | Aniket Verma (SRH) | 6.2 /10 | +11.4% | ||||||
| 💡 43 off 18 balls (SR 238.88) — lower-order blitz pushed SRH past 200 | |||||||||
| 8 | Heinrich Klaasen (SRH) | 5.4 /10 | +9.8% | ||||||
| 💡 31 off 22 balls — anchored the recovery stand with Kishan after top-order collapse | |||||||||
| 9 | Romario Shepherd (RCB) | 5.1 /10 | +8.6% | ||||||
| 💡 3/54 — expensive but took 3 key wickets in middle and death overs | |||||||||
| 10 | Bhuvneshwar Kumar (RCB) | 4.3 /10 | +6.2% | ||||||
| 💡 1/31 in 4 overs — maintained control from one end, economy of 7.75 on a 200+ pitch | |||||||||
| 11 | David Payne (SRH) | 4 /10 | +5.4% | ||||||
| 💡 2/35 in 3 overs — best of SRH's bowling attack, dismissed Salt and Padikkal | |||||||||
| 12 | Tim David (RCB) | 3.2 /10 | +3.8% | ||||||
| 💡 16* off 10 balls — composed finish, hit the winning boundary | |||||||||
| 13 | Suyash Sharma (RCB) | 3 /10 | +3.2% | ||||||
| 💡 1/28 in 3 overs — leg-spinner contained in the middle overs | |||||||||
| 14 | Travis Head (SRH) | 1.2 /10 | -4.8% | ||||||
| 💡 11 off 9 balls — fell to Duffy early, failed to provide the explosive start SRH needed | |||||||||
| 15 | Abhishek Sharma (SRH) | 0.8 /10 | -6.2% | ||||||
| 💡 7 off 8 balls — dismissed by Duffy in powerplay, SRH's captain failed to set the tone | |||||||||
⚠️ Early season data. Impact scores become more reliable after 5+ matches. Current ratings reflect 1 match only — rankings will shift significantly as the season progresses.
🎯 Betting Insight: What If They're Missing?
When key players are ruled out, check the win probability drop before placing your bet.
| Player | Team | If Missing |
|---|---|---|
| Virat Kohli | RCB | -15% |
| Jacob Duffy | RCB | -14% |
| Ishan Kishan | SRH | -13% |
| Travis Head | SRH | -12% |
| Devdutt Padikkal | RCB | -11% |
| Heinrich Klaasen | SRH | -10% |
| Abhishek Sharma | SRH | -9% |
| Philip Salt | RCB | -8% |
Methodology
Our AI prediction model processes every ball of every IPL 2026 match, calculating real-time win probability for both teams throughout the innings.
Each player receives credit (or debit) for how their actions — runs scored, balls faced, wickets taken, economy rate, catches, run-outs — shifted the win probability in their team's favour. This produces three sub-scores:
- Batting Impact: Win probability shifted through runs scored, strike rate, and batting phase (powerplay runs count differently to death overs runs).
- Bowling Impact: Win probability shifted through wickets, economy control, and bowling phase. A powerplay wicket in a chase is worth more than a 20th-over consolation wicket.
- Fielding Impact: Catches, run-outs, and direct hits measured by the win probability swing they caused. A boundary-line catch in the 18th over of a tight chase is high-impact.
The composite Impact Score (0-10) weights all three and normalises across the season. The Clutch Rating isolates performance in high-leverage moments (win probability between 30-70%).
This is original data from CricketPrediction's AI model. It is not available on any other platform. See our prediction track record for model accuracy.
Frequently Asked Questions
What is the AI Player Impact Index?
Our AI Player Impact Index measures how much each player shifts their team's win probability during a match. Unlike traditional stats (runs scored, wickets taken), this index captures the actual match-winning value of each contribution — a 30 off 12 in a close chase is worth more than 50 off 40 in a dead rubber.
How is the impact score calculated?
Our AI prediction model calculates win probability at every ball of every match. Each player's batting, bowling, and fielding contributions are measured by how much they moved win probability in their team's favour. The impact score is a 0-10 composite rating combining all three contributions.
What does "Win Prob Shift" mean?
Win Probability Shift is the total percentage points a player moved their team's win probability during a match. For example, +22.5% means the player's contributions increased their team's chances of winning by 22.5 percentage points across the entire match.
What is the Clutch Rating?
The Clutch Rating (A+ to D) measures performance in high-pressure moments — when the match outcome is genuinely uncertain (win probability between 30-70%). A player who delivers in tight situations earns a higher clutch grade than one who pads stats in already-decided matches.
What does "Absence Impact" show?
Absence Impact estimates how much a team's win probability drops if that player is rested, injured, or unavailable. For example, -15% means the team's win probability drops by 15 percentage points without that player. This is especially useful for betting when key players are ruled out.
How often is the Player Impact Index updated?
The index is updated after every IPL 2026 match. Impact scores become more reliable as the season progresses — after 5+ matches, the ratings stabilise and provide a strong predictive signal for remaining fixtures.
Why is this different from Orange Cap and Purple Cap?
Orange Cap (most runs) and Purple Cap (most wickets) measure volume, not impact. A player can score 400 runs in easy chases and rank high on Orange Cap while contributing little to actual wins. Our index measures what matters: did your performance change the result?
Can I use the Player Impact Index for betting?
Yes — the Absence Impact column is directly useful for betting. When a key player is confirmed out, check their absence impact to gauge how much the team's probability shifts. Always combine this with other factors (venue, conditions, opponent) and gamble responsibly.
More IPL 2026 Analysis
Player impact data is for informational purposes. Betting involves risk. Never wager more than you can afford to lose. Gamble responsibly.