Match Prediction — the IPL Super Contest guide.

How IPL Super Contest predicts matches

Every match prediction on IPL Super Contest is a probability, not a pick. We publish the inputs (form, venue, head-to-head, weather, toss) and the output (win probability) side by side so you can disagree with the output and use the inputs to build your own projection. The desk does not hide the math. The math is the product.

The model is a weighted average of five inputs, each scaled to a 0-100 range. The weights are tuned each off-season against the previous three seasons' match data. The current weights — form 0.28, venue 0.22, head-to-head 0.18, weather 0.12, toss 0.20 — are published on the methodology page.

Why we publish probabilities, not picks

A probability is honest. A pick is marketing. The desk uses probabilities to drive its own fantasy decisions — a 65-percent favourite at a flat deck with a clear captain candidate is a different decision than a 51-percent favourite at a slow deck with two equally good captain candidates. The probability tells you how much conviction to bring to the contest.

How IPL Super Contest predicts matches

Every match prediction on IPL Super Contest is a probability, not a pick. We publish the inputs (form, venue, head-to-head, weather, toss) and the output (win probability) side by side so you can disagree with the output and use the inputs to build your own projection. The desk does not hide the math. The math is the product.

The model is a weighted average of five inputs, each scaled to a 0-100 range. The weights are tuned each off-season against the previous three seasons' match data. The current weights — form 0.28, venue 0.22, head-to-head 0.18, weather 0.12, toss 0.20 — are published on the methodology page.

Why we publish probabilities, not picks

A probability is honest. A pick is marketing. The desk uses probabilities to drive its own fantasy decisions — a 65-percent favourite at a flat deck with a clear captain candidate is a different decision than a 51-percent favourite at a slow deck with two equally good captain candidates. The probability tells you how much conviction to bring to the contest.

Step 01

Match Prediction — the first thing to understand

Start with the rulebook, not the picks. The first thing every fantasy player should understand is the points system for the format they are about to play. A wicket in T20 is worth 25 base points plus an 8-point LBW/Bowled bonus — that single number changes how you value strike bowlers versus containment bowlers.

Form is the first input. We use the same five-innings weighted index as the rest of the site, scaled 0-100 for the team average. A team whose top seven batters all carry form indices above 70 scores a form input of 80. A team whose top seven includes three below-50 indices scores a form input of 45. The model multiplies the input by 0.28 and adds it to the total.

Form is most useful in the early season when the head-to-head record is short. By mid-season, the form index carries less marginal information because the head-to-head record starts to dominate. The model re-weights dynamically.

Form is the first input. We use the same five-innings weighted index as the rest of the site, scaled 0-100 for the team average. A team whose top seven batters all carry form indices above 70 scores a form input of 80. A team whose top seven includes three below-50 indices scores a form input of 45. The model multiplies the input by 0.28 and adds it to the total.

Form is most useful in the early season when the head-to-head record is short. By mid-season, the form index carries less marginal information because the head-to-head record starts to dominate. The model re-weights dynamically.

Step 02

Match Prediction — reading form the right way

Form is not the last match, it is the last 5 matches weighted by opposition quality. A fifty against a top-3 attack is worth more than a fifty against the league's weakest side. Our form index weights by opposition rank.

Venue is the second input. We use a normalised score based on the team's average first-innings total at the ground over the last three seasons. Wankhede averages 178, Chepauk 162, Eden Gardens 169. The venue input rewards teams with a venue bias and penalises teams that struggle at certain grounds.

Venue also includes a boundary factor. Short boundaries (Chinnaswamy, Dharamsala) favour power-hitting teams; long boundaries (Mohali, Lucknow) favour strike-rotation teams. The boundary length is published on the match prediction hub with every match.

Venue is the second input. We use a normalised score based on the team's average first-innings total at the ground over the last three seasons. Wankhede averages 178, Chepauk 162, Eden Gardens 169. The venue input rewards teams with a venue bias and penalises teams that struggle at certain grounds.

Venue also includes a boundary factor. Short boundaries (Chinnaswamy, Dharamsala) favour power-hitting teams; long boundaries (Mohali, Lucknow) favour strike-rotation teams. The boundary length is published on the match prediction hub with every match.

Step 03

Match Prediction — venue and pitch awareness

Wankhede, Eden Gardens and M. Chinnaswamy are different stadiums. The average T20 score at each is different, the boundary length is different and the spin-vs-pace ratio is different. Your team should reflect the venue, not ignore it.

Head-to-head is the third input. We use the last five meetings between the two franchises at any venue. A team that has won four of the last five meetings scores a head-to-head input of 80; a team that has won one of five scores 20. The model multiplies the input by 0.18 and adds it to the total.

Head-to-head decays after two seasons. Roster turnover in the IPL is high — a team that won four of five in 2022 has likely lost most of those players by 2026. The model applies a 0.6 weight to matches from the current season and 0.4 to matches from the previous season.

Head-to-head is the third input. We use the last five meetings between the two franchises at any venue. A team that has won four of the last five meetings scores a head-to-head input of 80; a team that has won one of five scores 20. The model multiplies the input by 0.18 and adds it to the total.

Head-to-head decays after two seasons. Roster turnover in the IPL is high — a team that won four of five in 2022 has likely lost most of those players by 2026. The model applies a 0.6 weight to matches from the current season and 0.4 to matches from the previous season.

Step 04

Match Prediction — captaincy as a multiplier

Captaincy is the single largest swing in your weekly score. A captain who scores 80 fantasy points with a 2× multiplier delivers 160 — the same player's un-captained score delivers 80. Visit the captain & vice-captain guide for the full framework.

Weather is the fourth input. We use a dew-adjusted chase-side win rate. A dry surface with high dew probability gives the chasing team a 6-8 percent boost; a wet surface with low dew gives the batting-first team a 3-5 percent boost. The dew probability is estimated from the hour-by-hour weather feed at match start.

Weather is the input the desk is least confident about. The model cannot see the pitch until the toss and cannot feel the dew until the second innings. Treat the weather input as a ±3 percent adjustment, not a ±10 percent one.

Weather is the fourth input. We use a dew-adjusted chase-side win rate. A dry surface with high dew probability gives the chasing team a 6-8 percent boost; a wet surface with low dew gives the batting-first team a 3-5 percent boost. The dew probability is estimated from the hour-by-hour weather feed at match start.

Weather is the input the desk is least confident about. The model cannot see the pitch until the toss and cannot feel the dew until the second innings. Treat the weather input as a ±3 percent adjustment, not a ±10 percent one.

Step 05

Match Prediction — what the model gets wrong

Every model gets something wrong. Ours under-weights the impact of a player returning from injury in their first two matches back, and over-weights the most-recent match. The methodology page lists every known bias.

Toss is the fifth input. We use a venue-specific toss-choice record. Some venues (Eden Gardens, Wankhede) have captains who choose to chase more than 70 percent of the time. The model applies the historical choice to the toss and adjusts the chase-side boost accordingly.

The toss input is updated within 30 seconds of the toss and republished. The pre-toss number is published 15 minutes before the toss; the post-toss number is published within 60 seconds of the captain's choice.

Toss is the fifth input. We use a venue-specific toss-choice record. Some venues (Eden Gardens, Wankhede) have captains who choose to chase more than 70 percent of the time. The model applies the historical choice to the toss and adjusts the chase-side boost accordingly.

The toss input is updated within 30 seconds of the toss and republished. The pre-toss number is published 15 minutes before the toss; the post-toss number is published within 60 seconds of the captain's choice.

Step 06

Match Prediction — putting it together

Apply the framework in this order: points system → form index → venue → captaincy → hedge. Skip any step and the model output degrades. The how to play page walks through the same checklist on a single match.

Putting the five inputs together gives a final probability in the 0-100 range. The probability is the model's output. It is not a guarantee; it is a probability distribution. A 60-percent favourite still loses 40 percent of the time. The probability tells you how much conviction to bring to your fantasy decisions.

The desk publishes a long-run accuracy log on the match prediction hub. The current 3-season rolling accuracy is 64 percent on match outcome and 71 percent on contest-tier predictions. The numbers are below the betting-market benchmark but above the public-pick benchmark.

Putting the five inputs together gives a final probability in the 0-100 range. The probability is the model's output. It is not a guarantee; it is a probability distribution. A 60-percent favourite still loses 40 percent of the time. The probability tells you how much conviction to bring to your fantasy decisions.

The desk publishes a long-run accuracy log on the match prediction hub. The current 3-season rolling accuracy is 64 percent on match outcome and 71 percent on contest-tier predictions. The numbers are below the betting-market benchmark but above the public-pick benchmark.