Player rankings

Rankings through 2026-10-26, using the preceding five years of internationals.

Men and women, and each format, are fitted separately. All players in the window are included, including those now retired.

Download all rankings · Fit details and provenance

Ratings are additive log win/loss odds. They are model associations, not WAR. Ranks apply only within a format, gender, and connected comparison group. An asterisk marks fewer than 20 appearances; exact tied ranks are retained.

Test — Men

182 matches; 20 draws/ties; 399 players.

C = 0.01; selected using the final year’s validation matches.

Full men’s Test table

Rank	Player	Teams	Rating	Matches
1	NM Lyon	Australia	0.075	40
2	TM Head	Australia	0.071	46
3	MA Starc	Australia	0.066	45
4	AT Carey	Australia	0.064	48
5	C Green	Australia	0.059	32
6	SPD Smith	Australia	0.057	46
7	M Labuschagne	Australia	0.054	45
8	UT Khawaja	Australia	0.052	40
9	AK Markram	South Africa	0.048	26
10	T Bavuma	South Africa	0.046	23
11	HC Brook	England	0.042	40
12	T de Zorzi*	South Africa	0.040	16
13	PJ Cummins	Australia	0.038	38
14	K Verreynne	South Africa	0.037	28
15	SM Boland	Australia	0.036	20
16	KA Maharaj	South Africa	0.034	25
17	PWA Mulder*	South Africa	0.034	18
18	RD Rickelton*	South Africa	0.034	16
19	M Jansen	South Africa	0.033	23
20	K Rabada	South Africa	0.032	27

Test — Women

11 matches; 2 draws/ties; 72 players.

C = 0.1; untuned, sparse-data estimates using the specified default.

Full women’s Test table

Rank	Player	Teams	Rating	Matches
1	D Brown*	Australia	0.104	5
2	YH Bhatia*	India	0.098	3
3	RS Gayakwad*	India	0.088	3
3	Renuka Singh*	India	0.088	3
3	P Vastrakar*	India	0.088	3
6	DB Sharma*	India	0.077	5
6	H Kaur*	India	0.077	5
6	S Mandhana*	India	0.077	5
6	S Rana*	India	0.077	5
6	JI Rodrigues*	India	0.077	5
6	Shafali Verma*	India	0.077	5
12	P Litchfield*	Australia	0.073	5
13	G Voll*	Australia	0.067	2
14	AJ Healy*	Australia	0.060	6
14	BL Mooney*	Australia	0.060	6
14	A Sutherland*	Australia	0.060	6
14	A King*	Australia	0.060	6
14	A Gardner*	Australia	0.060	6
14	EA Perry*	Australia	0.060	6
14	TM McGrath*	Australia	0.060	6

ODI — Men

600 matches; 0 draws/ties; 840 players.

C = 0.01; selected using the final year’s validation matches.

Full men’s ODI table

Rank	Player	Teams	Rating	Matches
1	Kuldeep Yadav	India	0.089	54
2	Shubman Gill	India	0.077	59
3	HH Pandya	India	0.075	29
4	RA Jadeja	India	0.068	42
5	M Theekshana	Sri Lanka	0.064	47
6	MA Starc	Australia	0.062	32
7	Mohammed Shami	India	0.061	28
8	SPD Smith	Australia	0.058	40
9	Fakhar Zaman	Pakistan	0.057	37
10	Haris Rauf	Pakistan	0.057	47
11	Babar Azam	Pakistan	0.056	54
12	WA Young	New Zealand	0.056	58
13	KL Rahul	India	0.052	58
14	RG Sharma	India	0.052	57
15	MG Bracewell	New Zealand	0.050	43
16	Ishan Kishan	India	0.049	25
17	Mohammed Siraj	India	0.047	46
18	Shaheen Shah Afridi	Pakistan	0.045	46
19	SM Sharif	Scotland	0.044	46
20	Agha Salman	Pakistan	0.044	49

ODI — Women

317 matches; 0 draws/ties; 463 players.

C = 0.1; selected using the final year’s validation matches.

Full women’s ODI table

Rank	Player	Teams	Rating	Matches
1	A King	Australia	0.390	54
2	A Gardner	Australia	0.318	55
3	TM McGrath	Australia	0.309	54
4	S Mandhana	India	0.293	57
5	M Kapp	South Africa	0.293	42
6	BL Mooney	Australia	0.287	54
7	NR Sciver-Brunt	England	0.272	49
8	EA Perry	Australia	0.263	51
9	H Deol	India	0.263	34
10	KJ Garth	Australia	0.260	33
11	T Brits	South Africa	0.258	49
12	ML Schutt	Australia	0.237	48
13	ME Bouchier	England	0.236	20
14	RP Yadav*	India	0.230	12
15	HC Knight	England	0.219	44
16	CE Dean	England	0.217	50
17	Shafali Verma	India	0.217	27
18	A Sutherland	Australia	0.211	49
19	P Litchfield	Australia	0.210	42
20	HNK Jensen*	New Zealand	0.210	10

T20I — Men

2443 matches; 0 draws/ties; 3671 players.

C = 0.1; selected using the final year’s validation matches.

Full men’s T20I table

Rank	Player	Teams	Rating	Matches
1	SA Yadav	India	0.576	99
2	MR Marsh	Australia	0.452	51
3	JC Buttler	England	0.443	72
4	JJ Bumrah	India	0.415	42
5	AR Patel	India	0.400	88
6	DA Miller	South Africa	0.398	44
7	K Rabada	South Africa	0.388	38
8	AJ Hosein	West Indies	0.378	90
9	JR Hazlewood	Australia	0.376	36
10	HC Brook	England	0.376	64
11	S Dube	India	0.370	55
12	RK Singh	India	0.370	41
13	AU Rashid	England	0.353	82
14	NT Ellis	Australia	0.351	35
15	IS Sodhi	New Zealand	0.341	75
16	JP Inglis	Australia	0.339	46
17	P Murungi	Uganda	0.332	33
18	Shadab Khan	Pakistan	0.330	61
19	Mohammad Nawaz	Pakistan	0.317	66
20	HH Pandya	India	0.310	82

T20I — Women

1600 matches; 0 draws/ties; 2315 players.

C = 0.1; selected using the final year’s validation matches.

Full women’s T20I table

Rank	Player	Teams	Rating	Matches
1	A King	Australia	0.498	29
2	BL Mooney	Australia	0.482	62
3	TM McGrath	Australia	0.444	52
4	S Mandhana	India	0.418	91
5	DB Sharma	India	0.416	96
6	CA Henry	West Indies	0.413	46
7	CE Dean	England	0.410	56
8	S Jafta	South Africa	0.408	55
9	BMSM Kumari	Sri Lanka	0.404	69
10	DN Wyatt	England	0.395	64
11	EA Perry	Australia	0.390	56
12	P Marange	Zimbabwe	0.389	34
13	S Ecclestone	England	0.388	64
14	Shafali Verma	India	0.388	88
15	ML Schutt	Australia	0.379	48
16	SIR Dunkley	England	0.356	58
17	AE Jones	England	0.352	73
18	J Nkomo	Zimbabwe	0.347	49
19	Tuba Hassan	Pakistan	0.346	50
20	ER Oza	United Arab Emirates	0.338	97

Reading the ratings

These are player-only lineup ratings: one coefficient for each player, with controls for the toss and batting first. The five-year window averages performance across that period. It includes retired players and does not claim to measure instantaneous form. Scores can be compared within each table, but not between formats or men’s and women’s competitions.

The predictor adds the eleven player coefficients on one side and subtracts those on the other. Replacing player \(j\) with player \(i\) changes the predictor by \(\beta_i-\beta_j\). A difference of 0.2 multiplies modeled win/loss odds by \(\exp(0.2)\approx1.22\). In a binary match model, moving from 50% win probability would then mean about 55%. In Tests this comparison conditions on a decisive result; the model separately predicts draws.

Cross-team matches connect the ratings. Stable teammate combinations still make individual attribution difficult, and omitted team conditions can be assigned to players. Regularization supplies a particular allocation of shared performance. The CSVs identify players with identical participation patterns, appearance counts, and each player’s largest teammate-overlap share. A connected schedule alone does not establish that every player’s contribution is separately identified.

The Test model retains draws and ties in a symmetric three-outcome model with a common draw propensity. Weather and playing conditions are not modeled separately. ODI and T20I tables use decisive matches. All formats require unambiguous fixed XIs; recorded match replacements are excluded. Cricsheet coverage is incomplete and currently withholds some Afghanistan-related matches. Counts, exclusions, source hashes, tuning scores, and fitted coefficients are included with the publication.

These point estimates do not have rank intervals and are not wins above replacement. Sorting them does not remove uncertainty in attribution. See the format-ranking specification for the estimator, tuning rules, and checks, and the ODI forecast comparison for the earlier out-of-sample experiment. The final-year scores used to select regularization in these new fits are tuning results, not an independent evaluation.

Earlier ODI experiment

The historical ODI rankings retain the saved 2023–2025 fit cutoffs, team-plus-player and player-only variants, and three- and five-year windows. They are separate from the new format tables. The original experiment specification and saved analysis remain intact; the user-requested ranking extensions are documented separately.