IPL Score Prediction 2026

Predicting match totals in the IPL is one of the most valuable skills for cricket analysis. Whether a match produces 350+ combined runs or stays below 300 depends on a handful of measurable factors. Understanding these factors, and how they interact at specific venues, turns guesswork into informed forecasting.

This page from iplpredict.in covers the key elements that determine IPL match scores, average totals at major venues, and a practical framework for predicting innings totals before a ball is bowled.

Factors That Determine IPL Match Scores

1. Venue and Pitch Type

The single biggest factor in score prediction is the ground. A match at Chinnaswamy (Bangalore) produces 20-30 more runs on average than one at Chepauk (Chennai). This is not random. Boundary dimensions, altitude, soil type, and pitch preparation all create predictable scoring environments. The same batting lineup will post vastly different totals depending on where they play.

2. Team Batting Depth

Teams with genuine batters through positions 1-8 consistently post higher totals than top-heavy lineups. When your number 7 can clear the boundary, captains can take more risks in the powerplay knowing the middle order provides a safety net. Teams like MI and KKR, with deep batting orders, tend to exceed venue averages more often than teams relying on 3-4 key batters.

3. Powerplay Approach

The first six overs set the tone for the innings. Aggressive powerplay teams (scoring 55+ in the first six) consistently finish with higher totals. Data from recent IPL seasons shows a strong correlation: every 10 extra runs in the powerplay translates to roughly 15-18 extra runs in the final total. Teams that start cautiously rarely recover the momentum later.

4. Conditions: Dew, Wind, and Time

Second innings scores at dew-heavy venues are typically 8-15 runs higher than first innings scores. The wet ball skids on, boundaries come easier, and bowlers lose their grip. Wind patterns at open stadiums (like Ahmedabad) can affect six-hitting. Afternoon matches tend to produce slightly lower scores than evening matches due to the ball swinging more in cooler conditions.

5. Match Context

Teams chasing realistic targets score differently than teams setting targets. A team chasing 160 often wins with 3-4 overs to spare, finishing around 162. The same team batting first might have posted 175 due to different risk calculations. Predicting the first innings score is more useful because the second innings is reactive.

Average Scores at Key IPL Venues

These averages are based on the last three IPL seasons (2023-2025) and account for both innings:

• Wankhede Stadium, Mumbai: Average first innings score: 182. Flat pitch, short boundaries, and Mumbai’s humid air makes the ball fly. Anything above 190 is a strong total. Below 165 is below par.
• M. Chinnaswamy Stadium, Bangalore: Average first innings score: 185. The highest-scoring IPL venue. At 920m altitude, the thinner air means the ball travels further. Par score is 185-190. Totals below 170 are chaseable.
• Eden Gardens, Kolkata: Average first innings score: 172. A balanced venue that offers something for bowlers early. The pitch flattens out after the powerplay. Par is around 170-175.
• M.A. Chidambaram Stadium, Chennai: Average first innings score: 158. The lowest-scoring major IPL venue. Slow pitch, turn for spinners, and big boundaries on one side suppress scoring. A total of 165+ is competitive. Anything above 175 is very difficult to chase here.
• Narendra Modi Stadium, Ahmedabad: Average first innings score: 170. A big ground that rewards good batting but punishes poor shot selection. The straight boundaries are long, so timing matters more than power.
• Arun Jaitley Stadium, Delhi: Average first innings score: 175. A batting-friendly surface that gets better under lights. Dew makes second innings scoring easier, so first innings scores need to be 180+ to be truly competitive.
• Rajiv Gandhi International Stadium, Hyderabad: Average first innings score: 168. Variable bounce can trouble batters. Some matches produce 200+ totals, others are grind-it-out 150 affairs. Check the pitch on match day.

A Practical Score Prediction Framework

Before each IPL 2026 match, work through this checklist:

1. Start with the venue average as your baseline first innings score
2. Adjust +5 to +10 runs if the batting team has a deep lineup and aggressive powerplay approach
3. Adjust -5 to -10 runs if the bowling team has a strong new-ball attack or quality death bowlers
4. Adjust for conditions: +5 for flat pitch reports, -5 for green tinge, +5 for dew in second innings
5. Consider the head-to-head: Some bowling attacks consistently trouble specific batting lineups

This framework gives you a predicted first innings total. For the second innings, assume the chasing team will reach the target with 5-8 balls to spare if the total is below par, or fall short by 10-15 runs if the total is above par. Use this alongside our toss prediction analysis for a complete match forecast.

Frequently Asked Questions

What is a good score in the IPL?

A “good score” depends entirely on the venue. At Chinnaswamy (Bangalore), 180 is par and 200+ is strong. At Chepauk (Chennai), 165 is par and 175+ is a match-winning total. The average IPL first innings score across all venues is approximately 170. Any total 10+ runs above the specific venue average should be considered a strong score that puts the batting team in a favorable position.

Which IPL venue has the highest average score?

M. Chinnaswamy Stadium in Bangalore consistently records the highest average scores in the IPL. The combination of a flat batting pitch, short straight boundaries, high altitude (920m above sea level), and Bangalore’s pleasant evening weather creates ideal batting conditions. Average first innings scores at Chinnaswamy over the last three seasons have exceeded 185, with multiple matches producing totals above 200.

How accurate are IPL score predictions?

Venue-based score prediction models can forecast first innings totals within a range of 15-20 runs about 70% of the time. The key variables that cause larger deviations are unexpected pitch behavior (a surface that cracks or offers unusual bounce), individual player brilliance (one batter scoring 90+ off 40 balls), and rain interruptions that change the match dynamics. No model can account for everything, but combining venue data with team matchup analysis gives you a reliable starting point for each match.