IPL 2027 champion forecast — report

All numbers below come from python run.py (full tables: outputs/results.md). Data cutoff: 2026-05-01 (match 43 of the 2026 league stage). This is a conditional, pre-tournament forecast. Read the limitations before you quote any single team's number.

1. Dataset quality and limitations

Checks and fixes:

• Deccan Chargers are merged into Sunrisers Hyderabad. The alias table gives both franchises team_id 2. SRH was a new franchise in 2013, so the code splits them: id 2 up to 2012 is DCH, from 2013 it is SRH. Delhi Daredevils→Capitals and Kings XI→Punjab Kings are the same franchise under a new name, and stay merged.
• No-result rows have a fake winner. match_winner is filled in for all 9 no-results, mostly with team2. Those rows are excluded from training, evaluation and Elo.
• Ties (16) carry the super-over winner, which is the official result. Elo updates them as draws.
• team1 is post-toss information. It is the side batting first 75.2% of the time. Each match's two teams are therefore ordered alphabetically, never by team1.
• Missing matches. Matches abandoned without a toss are absent (e.g. 71 rows for 2024). A few no-results are known to be missing from the points tables.
• Smaller gaps. 59 venue spellings for about 40 grounds; 51 missing cities (all in the UAE); win_by_runs agrees with ball-by-ball totals in 97.8% of matches (the rest look like rain-shortened games).
• Playoffs are the rows with no match_number: 3 per season in 2008–09, 4 afterwards. The champions derived from the data match the historical record (tested for 2009 and 2025).
• Format changes: 8 teams (2008–10, 2014–21), 10 teams (2011, 2022–26), 9 teams (2012–13). Semi-finals until 2010, the Qualifier/Eliminator format since 2011.

Not in the data: 2026 results after 1 May, the 2026 champion, any squad or retention/auction information, the 2027 squads, schedule, groups or rules, injuries or availability, and player-to-team mappings outside matches already played.

2. Prediction problem and models

There are three separate problems:

• Match outcome: P(A beats B) for one fixture.
• Qualification: P(top 4 after the league stage).
• Champion: P(winning the playoffs).

The 2027 question is a pre-tournament problem. Nothing from 2027 is observed, so the champion probability comes from simulating whole seasons, not from a classifier.

shrink and τ are fitted by Monte Carlo marginal likelihood of whole past seasons. The simulation then averages over the full (shrink, τ) posterior instead of plugging in one estimate.

An ensemble (Elo+LR average) was tested and rejected (Section 4).

Leakage controls

• Rolling-origin backtest: for test season S, every hyperparameter and model weight uses seasons up to S−1 only.
• Rolling features use shift(1), so a match's features see only that team's earlier matches. A unit test confirms that changing a match's own result doesn't change its features.
• Toss and playing XIs are never used in the main models. Toss appears only in one clearly labelled post-toss variant.
• Pre-tournament forecasts are frozen at season start; only the published fixture list is used, never results.

3. Historical backtests (out of sample)

Match level (pre-match and pre-toss; in-season Elo updating; 882 matches from 2013–2026):

Paired bootstrap of the log-loss difference (95% interval):

• Elo − coin: +0.0010 [−0.0069, +0.0098], no detectable difference.
• LR − Elo: +0.0046 [+0.0003, +0.0088], LR is worse.
• GBM − Elo: +0.0247 [+0.0123, +0.0373], GBM overfits.
• The toss adds nothing: −0.0003 [−0.0021, +0.0016].

Calibration (outputs/calibration.png): predictions sit between 0.38 and 0.63, and the observed rates in each bin are noisy around 0.5.

Pre-tournament level (frozen at season start; 830 league matches): log loss 0.6952 vs 0.6931 for a coin flip.

Tournament level: pre-season title probabilities for 2013–2025, using the real fixtures and playoff formats, scored on the log probability given to the actual champion.

Before 2022 the model gained real ground on uniform, because CSK and MI were persistently strong. Since the 2022 expansion and the 2022/2025 mega-auctions, prior ratings have been worse than uniform: champions GT 2022, KKR 2024 and RCB 2025 were ranked 5th, 9th and 10th pre-season.

4. Comparison with baselines and model choice

No match model beats a coin flip with any statistical confidence. IPL match outcomes are close to a coin toss given team-level history. Elo is kept because:

• it is the best non-trivial model, statistically tied with the coin;
• it beats LR, GBM and the ensemble;
• it supplies the strength scale the simulator needs.

The extra features (run-rate differential, form) carry no out-of-sample signal: the form coefficient is even slightly negative. The ensemble is rejected (+0.0018 log loss vs Elo).

5–7. IPL 2027 forecast (20,000 simulated seasons)

• Champion probabilities sum to 100.0%, top-4 to 400% and final to 200% (asserted in the tests).
• The Monte Carlo standard error is about 0.24 percentage points.
• Chart: outputs/ipl2027_champion_probabilities.png.

How to read it: every team is between 8% and 13%, against 10% for picking at random. Neighbouring teams in this ranking (e.g. ranks 4–8, 9.9% vs 8.9%) are not meaningfully different. Given the 2022–25 backtest, even the gap between RCB and MI should be treated as weak evidence.

8. Key factors driving the estimates

1. End-of-data Elo ratings. These cover all of 2025 plus 2026 up to match 43. RCB (2025 champion), GT and PBKS (2025 finalist) rate highest; MI and CSK rate lowest.
2. Between-season regression (Elo carry-over 0.8, with the shrink posterior spread over 0.25–2.0). This pulls everyone toward the mean.
3. Strength uncertainty τ. The posterior favours small τ (0–25 Elo points), but the data barely separate the values. Raising τ to 150 compresses the title range to about 9.0–11.7% (tau sensitivity table).
4. Small effects: home advantage of 20 Elo points (the home team won 52.7% of 846 home matches) and the playoff format (top-2 finishers get two chances to reach the final).

9. Limitations and uncertainty

• Squads are the main unknown. Team-level Elo can't see retentions, the auction or injuries. That is the most likely reason the 2022–25 tournament backtest underperformed uniform.
• The 2026 season is incomplete (43 matches, ending 1 May 2026). The rest of 2026 and its champion happened in reality but are not in the dataset and were not invented.
• The 2027 format is assumed: the 2022–26 format, i.e. 10 teams, 14 league matches each, two groups (drawn at random in every simulation) and the Qualifier/Eliminator playoffs.
• Simplifications in the simulator. NRR is approximated by simulated margins. Rain no-results (worth 1 point each) are not simulated. Venues are home/away by fixture, with no ground-specific effects.
• Small samples. There are 13 tournament backtests, and the 9 seasons from 2013 to 2021 come from a different, dynasty-dominated era. The tournament-level skill estimate is very uncertain.
• The home-city map is static and hand-entered from public knowledge (e.g. it treats 2022 Mumbai games as MI home games).

10. Exact assumptions

• Data cutoff: 2026-05-01. All matches up to that date are used; no external data.
• Teams: the 10 franchises of 2026 (CSK, DC, GT, KKR, LSG, MI, PBKS, RCB, RR, SRH), assumed unchanged for 2027.
• Final Elo: K = 4, carry = 0.8 and home = 20, all selected on 2009–2026. One season-boundary regression step is applied from the mid-2026 ratings.
• Strength model: a posterior mixture over shrink ∈ {0.25…2.0} and τ ∈ {0…150}, fitted on 2010–2026 league results.
• Simulation: 20,000 seasons over 20 random group draws. Seeds are fixed, so the results reproduce exactly.

To make this a credible forecast, re-run once the 2027 squads exist (after retentions and the auction). The highest-value upgrade is player-level strength (ratings built from the ball-by-ball data and aggregated over each 2027 squad) to replace team-level carry-over. The full 2026 results should be added as soon as they are in the dataset.