IPL Auction Analysis: Where Do the Crores Go?

The Big Picture

IPL teams spent ₹687 Cr on disappointing players from 2012-2025. Using ball-by-ball data to compute WAR (Wins Above Replacement), we identify which purchases wasted the most money.

Top Disappointments (by Wasted Money)

Total wasted: ₹687 Cr across 378 disappointing players

Year	Player	Team	Paid	Predicted	Actual	Wasted
2025	Ravi Bishnoi	LSG	₹11.0Cr	7.8	-0.7	₹8.6Cr
2014	Gautam Gambhir	KKR	₹12.5Cr	10.9	2.6	₹8.4Cr
2025	Shivam Dube	CSK	₹12.0Cr	7.2	-1.1	₹8.4Cr
2025	Tilak Varma	MI	₹8.0Cr	10.5	2.3	₹8.0Cr
2020	Glenn Maxwell	PBKS	₹10.8Cr	8.4	1.0	₹7.5Cr
2014	Chris Gayle	RCB	₹7.5Cr	12.2	0.3	₹7.5Cr
2015	Yuvraj Singh	DC	₹16.0Cr	9.2	2.1	₹7.2Cr
2018	Chris Morris	DC	₹11.0Cr	7.8	0.7	₹7.2Cr
2025	Sanju Samson	RR	₹18.0Cr	10.2	3.2	₹7.2Cr
2014	Virat Kohli	RCB	₹12.5Cr	12.0	5.0	₹7.1Cr
2014	Shane Watson	RR	₹12.5Cr	13.5	6.6	₹7.0Cr
2022	Mohammed Siraj	RCB	₹7.0Cr	7.6	-3.7	₹7.0Cr
2018	Manish Pandey	SRH	₹11.0Cr	6.6	0.0	₹6.7Cr
2022	Anrich Nortje	DC	₹6.5Cr	6.0	-0.9	₹6.5Cr
2014	Shikhar Dhawan	SRH	₹12.5Cr	10.1	3.7	₹6.5Cr

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Team Efficiency

Team	N	Spent	WAR	WAR/Cr
KKR	74	₹281Cr	295	1.05
SRH	78	₹311Cr	306	0.99
PBKS	76	₹285Cr	281	0.99
RCB	69	₹300Cr	280	0.93
CSK	60	₹313Cr	291	0.93
RR	72	₹310Cr	269	0.87
DC	83	₹348Cr	293	0.84
MI	68	₹330Cr	258	0.78
LSG	25	₹148Cr	114	0.77

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Backtest Results

How well can we predict player performance at auction time using only prior data?

Year	N	R²	Rank ρ	RMSE
2012	10	-0.22	0.56	4.9
2013	14	-1.29	-0.26	5.2
2014	125	-0.05	0.39	4.9
2015	17	-0.12	0.17	3.9
2016	43	0.01	0.17	3.9
2017	24	-0.28	-0.02	4.3
2018	116	0.14	0.32	5.2
2019	22	0.17	0.31	3.7
2020	28	-0.18	0.31	4.6
2021	31	-0.00	0.41	3.8
2022	150	0.12	0.37	5.3
2023	33	0.02	0.31	5.8
2025	43	0.29	0.55	4.8

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Methodology

WAR Calculation

From 3,137 player-season records of ball-by-ball data:

Batting WAR = (runs - expected_runs - dismissal_penalty) / RUNS_PER_WIN

• Expected runs = balls_faced × replacement_strike_rate
• Dismissal penalty = dismissals × RUNS_PER_DISMISSAL (6.0)

Bowling WAR = (replacement_runs - runs_conceded + wicket_bonus) / RUNS_PER_WIN

• Replacement runs = overs × replacement_economy
• Wicket bonus = wickets × RUNS_PER_WICKET (6.0)

Replacement levels: 15th percentile SR (batting), 80th percentile economy (bowling)

Empirically Estimated Constants

Constant	Value	Derivation
RUNS_PER_DISMISSAL	6.0	OLS regression: innings_runs ~ wickets_lost + overs_batted (n=2,677 T20I innings)
RUNS_PER_WICKET	6.0	Same regression; symmetric by assumption (see note below)
RUNS_PER_WIN	10	Logistic regression on match outcomes + close-game heuristic

Note on symmetry: RUNS_PER_DISMISSAL and RUNS_PER_WICKET are conceptually distinct (batting opportunity cost vs. bowling value), but the innings-level regression provides a single estimate for the marginal effect of a wicket on team total (~6 runs). We apply this symmetrically: a dismissed batter costs their team ~6 potential runs; a bowler taking a wicket saves their team ~6 runs. This is a modeling choice that simplifies the framework while remaining empirically grounded.

Wasted Money Calculation

For each player:

1. Predict WAR using only info available at auction (lagged IPL WAR, T20I history)
2. Compare to actual WAR
3. Wasted = (predicted - actual) × price_per_WAR, capped at price paid

Implied market price: ₹1.01 Cr per WAR

Prediction Model

XGBoost with expanding window training:

• Features: lag1/2/3 IPL WAR, T20I WAR, career totals, trends
• Training: all prior auction+outcome data
• Mean rank correlation: 0.28 (13 years evaluated)

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Repository Structure

ipl_auction/
├── data/
│   ├── acquisitions/         # Auction price data
│   └── perf/                 # IPL/T20I WAR by season
├── scripts/
│   ├── auction/              # Scraping, assembly
│   ├── perf/                 # Performance data processing
│   ├── prediction/           # WAR prediction, backtest
│   ├── retrospective/        # Hedonic regression
│   └── shared/               # Utilities
├── tabs/                     # Output tables (CSV)
│   ├── disappointments.csv   # Sorted by wasted money
│   ├── team_efficiency.csv
│   └── retroactive_*.csv
└── .github/workflows/        # Auto-update README

Quick Start

# Full pipeline
make all

# Just analysis
python3 scripts/prediction/06_comprehensive_backtest.py
python3 scripts/prediction/07_economic_analysis.py

# Update README tables
python3 scripts/update_readme_tables.py

Data Sources

Source	Coverage
Kaggle, Wikipedia, IPL official	Auction prices 2008-2026
Kaggle ball-by-ball	Match data 2008-2025
World Bank/RBI	CPI for inflation adjustment

Limitations

1. Missing years: 2024 auction data not available
2. Selection bias: ~19% of auctioned players never fielded
3. Private information: Teams have scouting data we don't

References

• Rosen, S. (1974). Hedonic prices and implicit markets. JPE.
• Scully, G. W. (1974). Pay and performance in MLB. AER.