IPL Prediction Today
Free runs probability for every batter in today's IPL 2026 match
Lucknow Super Giants vs Delhi Capitals
đ Lucknow  | đ 1 Apr 2026
đ¯ Top Batting Predictions, LSG
đ§ Rishabh Pant's prediction driven by excellent recent form (avg 47.0 last 3 innings), upward form trend, decent career average (34.8).
đ§ Josh Inglis's prediction driven by solid recent form (avg 27.0 last 3), declining form trend, decent career average (30.9).
đ§ Aiden Markram's prediction driven by solid recent form (avg 36.7 last 3), decent career average (31.3), high big-innings rate (18.2% innings with 50+).
đ§ Mitchell Marsh's prediction driven by excellent recent form (avg 83.0 last 3 innings), upward form trend, decent career average (29.4).
đ§ Matthew Breetzke's prediction driven by struggling for form lately (avg 14.0 last 3).
đ¯ Top Batting Predictions, DC
đ§ Nitish Rana's prediction driven by struggling for form lately (avg 13.7 last 3), decent career average (28.0), struggles vs Lucknow Super Giants (avg 15.0).
đ§ KL Rahul's prediction driven by excellent recent form (avg 52.7 last 3 innings), upward form trend, strong career average (47.5).
đ§ Prithvi Shaw's prediction driven by struggling for form lately (avg 13.3 last 3), declining form trend, high big-innings rate (17.7% innings with 50+).
đ§ Abishek Porel's prediction driven by struggling for form lately (avg 14.7 last 3), declining form trend, strong record vs Lucknow Super Giants (avg 36.3).
đ§ Karun Nair's prediction driven by career average of 24.9.
What Our AI Predicts
Batting Run Predictions
Individual score range forecasts for every batsman: Under 20, 20-49, and 50+ run probability.
Match Simulator
Simulate any IPL matchup and get instant win probability based on team data.
Player Analytics
Career stats, batting averages, strike rates, and AI performance predictions for every IPL player.
How CricNerd's IPL Predictions Work
CricNerd provides free cricket predictions for every IPL 2026 match. Our AI prediction engine analyses data from over 1,100 historical IPL matches, every ball-by-ball record from 2008 onwards, to generate probability-based forecasts using 25 features.
For each match, our model considers player career stats, recent form (last 3 innings), venue-specific conditions, opposition bowling attack, and player-vs-opponent matchup history to produce batting run bucket probabilities.
For individual players, we predict batting run buckets, the probability of scoring in specific ranges (Under 20, 20-49, and 50+). We also show the 20+ contribution probability, a high-confidence metric that captures the chance of a meaningful innings. Each player's prediction includes an AI-generated explanation highlighting the key factors driving the forecast.