PSL 2026 AI Player Impact Index
Who actually wins matches — measured by win probability, not vanity stats
Updated: · 12 matches analysed · Data: CricketPrediction AI Model
After 12 PSL 2026 matches, Sameer Minhas (ISU) leads our AI Player Impact Index with a match impact score of 9.7/10 — his batting shifted Islamabad United's win probability by 38.5%. 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.
200% Welcome Bonus up to ₹20,000
Asia's most trusted sportsbook · Best cricket odds · Fast INR withdrawals
Hit a Six in Over 1 — Get Paid as Winner
Cricket Early 6 Payout · 15% rakeback with code maximus88
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 |  Sameer Minhas (ISU) | 9.7 /10 | +38.5% | ||||||
| 💡 82* off 48 vs QG (128-run stand with Shadab) + 70 off 36 vs RWP. Over 170 runs in 3 innings at SR 160+. PSL 2026's breakout star. | |||||||||
| 2 | Shadab Khan (ISU) | 9.5 /10 | +35.2% | ||||||
| 💡 3/23 + 69* off 39 vs QG — match-winning all-round display. 5 wickets in the tournament. Captain leading from the front. | |||||||||
| 3 |  Sahibzada Farhan (MS) | 9.4 /10 | +34% | ||||||
| 💡 106* off 57 chasing 226 vs HK — innings of the tournament. 132 total runs. New franchise's talisman. | |||||||||
| 4 |  Hasan Ali (KRK) | 9.2 /10 | +30.5% | ||||||
| 💡 Maroon Cap leader — 8 wickets at avg 9.50, econ 6.33. KK's 3-0 start built on his bowling. 3/24 vs RWP standout. | |||||||||
| 5 |  Hasan Nawaz (QG) | 8.8 /10 | +26% | ||||||
| 💡 Orange Cap leader — 138 runs including 66* off 36 vs ISU. QG's most consistent performer despite team's 1W 3L record. | |||||||||
| 6 | Azam Khan (KRK) | 8.6 /10 | +24% | ||||||
| 💡 74 off 34 vs RWP — brutal hitting that sealed the chase. KK's x-factor in the middle order. | |||||||||
| 7 |  Shaheen Shah Afridi (LQ) | 8.5 /10 | +23% | ||||||
| 💡 6 wickets in 3 matches. Leading LQ's defence of their title with powerplay aggression. Joint second on Maroon Cap list. | |||||||||
| 8 | Steve Smith (MS) | 8.3 /10 | +21% | ||||||
| 💡 53 vs QG — anchored the chase of 167. Veteran steadying a new franchise. Test-match temperament in T20. | |||||||||
| 9 | Mohammad Naeem (LQ) | 8.2 /10 | +19.5% | ||||||
| 💡 103 runs in 3 matches including 60 vs MS in rain-reduced win. Stepped up with Fakhar banned. | |||||||||
| 10 | Shamyl Hussain (QG) | 8 /10 | +18% | ||||||
| 💡 127 runs in 4 matches. Consistent top-order scoring for QG despite their struggles. | |||||||||
| 11 | Abrar Ahmed (QG) | 7.9 /10 | +17.5% | ||||||
| 💡 6 wickets in 4 matches including 3/23 vs HK. Mystery spinner delivering on spin-friendly surfaces. | |||||||||
| 12 | David Warner (KRK) | 7.8 /10 | +16% | ||||||
| 💡 50 off 36 vs RWP. Veteran presence anchoring KK's unbeaten 3-0 start. Leadership in the field. | |||||||||
| 13 | Mohammad Nawaz (MS) | 7.7 /10 | +15.5% | ||||||
| 💡 3/30 vs QG — match-turning spell. Left-arm spin controlling the middle overs for the new franchise. | |||||||||
| 14 | Fakhar Zaman (LQ) | 7.5 /10 | +14% | ||||||
| 💡 53 off 39 in PSL opener vs HK. Now banned 2 matches (ball tampering — appeal dismissed). Returns Apr 11. | |||||||||
| 15 | Josh Philippe (MS) | 7.4 /10 | +13.5% | ||||||
| 💡 55 vs IU in Match 4 — helped MS to their first PSL win. Australian consistency for new franchise. | |||||||||
🎯 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 |
|---|---|---|
| Josh Philippe | MS | 6.5% |
| Azam Khan | KRK | 7% |
| Mohammad Naeem | LQ | 7% |
| Shamyl Hussain | QG | 7% |
| Mohammad Nawaz | MS | 7.5% |
| Hasan Nawaz | QG | 8% |
| Steve Smith | MS | 8% |
| Abrar Ahmed | QG | 8% |
Methodology
Our AI prediction model processes every ball of every PSL 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-pressure 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 PSL 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 PSL 2026 Analysis
Player impact data is for informational purposes. Betting involves risk. Never wager more than you can afford to lose. Gamble responsibly.