Cricket betting

India v Zimbabwe betting tips, prediction & odds

Women's ODI · ODI · Sunday 25 October at 08:30

Odds updated Sat, 24 Oct, 11:00 (UK time) · full refresh every day at 08:00, then hourly

Every number below comes from 9,116 ball-by-ball simulations of this match (model cricket-sim-1.0.0), run on real historical delivery data. Selections are not published as tips until the model passes backtesting.

Team news & expected XI

Official team sheets are used the moment they are published. Until then each XI is the model's own selection read: who this side has picked in this format, where they batted and how many overs they bowled. Form figures are exponentially weighted over the last year, so recent matches count for more.

India

Expected XI from selection history

Order	Player	Role	Plays	Form runs	Overs
1	Pratika Rawal	all-rounder	93%	36.5@ 82.0	2.8
2	S Mandhana	batter	97%	44.4@ 93.5	1.5
3	H Deol	batter	84%	30.3@ 74.8	1.0
4	H Kaur	batter	89%	38.8@ 83.8	0.0
5	JI Rodrigues	batter	97%	32.8@ 104.9	0.0
6	RM Ghosh	batter	97%	22.0@ 102.8	0.0
7	DB Sharma	all-rounder	97%	21.1@ 81.8	8.5
8	S Rana	bowler	55%	23.0@ 63.2	8.6
9	KS Gautam	bowler	40%	20.6@ 87.7	7.4
10	AB Kaur	bowler	26%	13.5@ 91.2	8.3
11	K Goud	bowler	43%	8.3@ 78.1	6.8
12	Renuka Singh	bowler	34%	0.6@ 19.3	9.0
13	N Shree Charani	bowler	72%	6.5@ 69.1	8.7

Last named squad (Australia v India, 2026-03-01): DB Sharma, H Deol, H Kaur, JI Rodrigues, KS Gautam, N Shree Charani, Pratika Rawal, RM Ghosh, Renuka Singh, S Mandhana, S Rana

Zimbabwe

Expected XI from selection history

Order	Player	Role	Plays	Form runs	Overs
1	SM Mayers	all-rounder	49%	6.9@ 67.7	4.3
2	M Mupachikwa	batter	89%	30.5@ 61.3	0.0
3	CS Mugeri-Tiripano	batter	80%	40.4@ 75.8	0.0
4	K Ndlovu	all-rounder	63%	19.6@ 62.8	7.8
5	CT Dhururu	batter	56%	8.4@ 33.7	0.0
6	A Ndiraya	batter	55%	35.3@ 76.2	1.4
7	L Tshuma	all-rounder	61%	26.3@ 74.5	6.7
8	B Biza	all-rounder	63%	23.3@ 65.8	4.1
9	J Nkomo	all-rounder	52%	25.8@ 73.3	8.0
10	R Pasipanodya	all-rounder	49%	16.7@ 49.1	2.0
11	A Mazvishaya	all-rounder	45%	18.1@ 61.6	7.4
12	N Sibanda	bowler	75%	7.1@ 44.6	6.2
13	L Mabhero	bowler	51%	2.5@ 30.3	6.1

Last named squad (Zimbabwe v Ireland, 2025-07-28): B Biza, CS Mugeri-Tiripano, CT Dhururu, F Chipare, K Chigora, K Ndlovu, L Tshuma, M Mavunga, M Mupachikwa, N Gwanzura, R Pasipanodya

Pitch & ground

BCA Stadium, Kotambi, Vadodara, India

ODI average across all grounds

Average 1st innings: 237.8
Average 2nd innings: 198.3
Runs per over: 5.11
Boundary balls: 903%
Pace economy: 5.35
Spin economy: 4.95
Powerplay RPO: 4.69
Death RPO: 6.92
Chasing wins: 5144%
Batting first wins: 4856%
Matches counted: 867

Weather at the start

No forecast is available: a match needs a confirmed ground with coordinates and a start time inside the next two weeks.

What we could not source

· No forecast is available for this ground yet.
· Pitch numbers shown are the format-wide average because this ground has no stored record.

Every live spread market

The full set of Spreadex spread lines currently priced on this match, exactly as captured. Sell below the lower figure, buy above the higher one. Where the simulation covers a market we show its expected makeup beside the price, the side that implies and the edge in points per £1 a point — comparisons, not tips, until the format clears backtesting.

Odds updated Sat, 24 Oct, 11:00 (UK time) · captured hourly from the live market

Match Result


India (W) to win match	96.5	100	66.7	sell	+29.8
Zimbabwe (W) to win match	0	3.5	32.6	buy	+29.1

Supremacy


India (W)/Zimbabwe (W)	116	126	33.6	sell	+82.4

More ODI spread markets across every priced match.

Model vs market

The simulation output next to the live bookmaker price on every market we hold a price for. Fixed-odds edges are expected value per £1 staked; spread edges are the expected points gained per £1 a point. Positive numbers are where the model disagrees with the price — they are comparisons, not published tips.

Honest health warning: across 29 replayed ODI matches the model’s match-winner probabilities have not yet beaten a simple coin-flip benchmark, so treat the match-winner comparisons below as work in progress rather than value. Run projections need separate validation; this does not establish a profitable team or player runs strategy.

What does “coin flip” mean?

It is a deliberately simple test: give each team a 50% chance in every decided, two-team match, without looking at players, form or the pitch. It does not mean every real match is evenly matched, or that our model literally tosses a coin.

We score the probability given to the actual winner, not just count correct picks. Lower is better. Always saying 50/50 scores about 0.69. A model saying 80% when that team loses gets a bigger penalty than one saying 55%. “Not better than chance” means its tested winner probabilities have not improved on that simple starting point — they are not a proven betting edge.

This test excludes draws and ties; it is not a three-outcome Test-match benchmark. Beating it still does not prove profit: the offered odds, bookmaker margin and settled returns matter too. A confidence score of 80/100 is not an 80% chance of winning.


Match winner	Zimbabwe	32.6% (3.07)	29.00	back	+845.6% EVFlagged
Supremacy	India (W)/Zimbabwe (W)	33.6	116 – 126	sell	+82.4 runs
Match result index	India to win match	66.7	96.5 – 100	sell	+29.8 pts
Match result index	Zimbabwe to win match	32.6	0 – 3.5	buy	+29.1 pts
Match winner	India	66.7% (1.50)	1.00	back	-33.3% EVFlagged

“Flagged” means the gap between the model and the price is too big to be a real edge — we read it as a sign the model is missing something about this match, and it is one of the checks that keeps tips switched off.

Match winner projection


India	66.7%	1.50	1.00	0.0%	-33.3%	96.5 – 100	60/100
Zimbabwe	32.6%	3.07	29.00	100.0%	+845.6%	0 – 3.5	44/100

India to win

Why we like it

Head-to-head: 0-0 in 2 stored ODI meetingsLOW sample
India have won 0 of the 2 completed ODI matches we hold between these sides, the most recent on 25 Oct 2026.
India full record
Projected runs: 160.9 vs 127.3
The simulation gives India a +33.6 run expected margin over Zimbabwe, from 9,116 simulated innings.

Risks

Model and market disagree too far to trust
Model 66.7% against 0.0% implied by 1.00 with the bookmaker margin stripped out — -33.3% per £1 staked. A gap this size is more likely a data problem than an edge, so it is flagged rather than tipped.
Winner probabilities have not passed backtesting
Across 29 replayed ODI matches the model's log loss is 0.75 against a 0.69 coin-flip baseline, so this chance is shown as a comparison, not a tip.

Zimbabwe to win

Why we like it

Head-to-head: 0-0 in 2 stored ODI meetingsLOW sample
Zimbabwe have won 0 of the 2 completed ODI matches we hold between these sides, the most recent on 25 Oct 2026.
Zimbabwe full record

Risks

Projected runs: 127.3 vs 160.9
The simulation gives Zimbabwe a -33.6 run expected margin over India, from 9,116 simulated innings.
Model and market disagree too far to trust
Model 32.6% against 100.0% implied by 29.00 with the bookmaker margin stripped out — +845.6% per £1 staked. A gap this size is more likely a data problem than an edge, so it is flagged rather than tipped.
Winner probabilities have not passed backtesting
Across 29 replayed ODI matches the model's log loss is 0.75 against a 0.69 coin-flip baseline, so this chance is shown as a comparison, not a tip.
Confidence 44/100, data depth 39/100
Confidence is scored from the ball-by-ball history behind these teams, how settled the XI is and how many simulations agreed.

Supremacy spread

India (W)/Zimbabwe (W)

Market 116 – 126 · model expected margin 33.6 runs

Model edge: sell +82.4 runs per £1 a point

Team runs projection

India

Projected 50-over innings total

161

median 149 · 10th–90th percentile 73–269

Wickets lost 2.6

Powerplay runs 41


120+	66.2%
140+	54.8%
160+	44.6%
180+	35.0%
200+	27.7%

confidence 60/100 · data 61/100

Zimbabwe

Projected 50-over innings total

127

median 119 · 10th–90th percentile 58–210

Wickets lost 4.0

Powerplay runs 36


120+	49.6%
140+	37.0%
160+	26.3%
180+	17.8%
200+	12.2%

confidence 44/100 · data 39/100

India runs

Why we like it

160.9 projected vs 237.8 parLOW sample
ODI average across all grounds averages 237.8 in the first innings over 867 matches; the model has India below that by 77.0 runs.
Middle of the range 149.0, 10th–90th 73–269
Eight innings in ten finish inside that band, with 2.6 wickets lost on average and 40.8 powerplay runs.

Risks

Model has run 3.8 runs high in testing
Measured over 29 replayed ODI matches, average error 62.5 runs.

Zimbabwe runs

Why we like it

127.3 projected vs 237.8 parLOW sample
ODI average across all grounds averages 237.8 in the first innings over 867 matches; the model has Zimbabwe below that by 110.5 runs.
Middle of the range 119.0, 10th–90th 58–210
Eight innings in ten finish inside that band, with 4.0 wickets lost on average and 35.5 powerplay runs.

Risks

Model has run 3.8 runs high in testing
Measured over 29 replayed ODI matches, average error 62.5 runs.

Player runs projection

Expected runs today, not a career average: each player's chance of batting, expected balls faced and the opposition attack are simulated together.


S Mandhana India	50.8	43	108	97.0%	63	74.6%	43.6%	—	—
Pratika Rawal India	43.0	36	91	93.0%	61	70.8%	35.8%	—	—
CS Mugeri-Tiripano Zimbabwe	30.1	18	80	80.0%	48	48.4%	23.8%	—	—
M Mupachikwa Zimbabwe	26.7	20	62	89.0%	53	51.2%	17.4%	—	—
H Deol India	23.9	14	64	84.0%	50	43.5%	17.3%	—	—
H Kaur India	17.7	5	54	89.0%	40	32.7%	11.6%	—	—
A Ndiraya Zimbabwe	12.5	0	42	55.0%	37	24.1%	7.2%	—	—
JI Rodrigues India	12.4	0	42	97.0%	30	23.5%	7.3%	—	—
SM Mayers Zimbabwe	10.2	0	34	49.0%	26	19.7%	4.7%	—	—
K Ndlovu Zimbabwe	10.1	2	31	63.0%	30	19.4%	3.4%	—	—
B Biza Zimbabwe	10.1	0	36	63.0%	35	19.9%	5.0%	—	—
L Tshuma Zimbabwe	7.6	0	26	61.0%	27	14.3%	2.3%	—	—
RM Ghosh India	6.8	0	25	97.0%	22	13.2%	3.2%	—	—
J Nkomo Zimbabwe	6.7	0	26	52.0%	31	13.4%	3.0%	—	—
CT Dhururu Zimbabwe	5.8	0	20	56.0%	25	10.0%	0.7%	—	—
R Pasipanodya Zimbabwe	4.3	0	17	49.0%	35	8.6%	1.2%	—	—
DB Sharma India	3.9	0	16	97.0%	25	8.0%	1.0%	—	—
A Mazvishaya Zimbabwe	3.3	0	13	45.0%	30	6.5%	0.7%	—	—
S Rana India	0.8	0	0	55.0%	15	1.2%	0.0%	—	—
KS Gautam India	0.7	0	0	40.0%	19	1.4%	0.1%	—	—
K Goud India	0.5	0	0	43.0%	19	0.8%	0.0%	—	—
AB Kaur India	0.3	0	0	26.0%	20	0.4%	0.0%	—	—

S Mandhana runs

Why we like it

Expected to face 63 balls at number 4
Opportunity drives the projection: 50.8 runs on average, 108 in the top 10% of simulations.
Recent form 44.4 runs an inningsLOW sample
Weighted over the last 3 innings at a strike rate of 93.5.
Against paceHIGH sample
1090 runs off 1184 balls in 48 ODI matches — strike rate 92.1 vs 90.5 overall, average 68.1 vs 49.1 overall.

Pratika Rawal runs

Why we like it

Expected to face 61 balls at number 2
Opportunity drives the projection: 43.0 runs on average, 91 in the top 10% of simulations.
Recent form 36.5 runs an inningsLOW sample
Weighted over the last 3 innings at a strike rate of 82.0.
Against paceHIGH sample
329 runs off 364 balls in 16 ODI matches — strike rate 90.4 vs 86.4 overall, average 109.7 vs 48.9 overall.

Risks

Against spinHIGH sample
374 runs off 444 balls in 13 ODI matches — strike rate 84.2 vs 86.4 overall, average 37.4 vs 48.9 overall.

CS Mugeri-Tiripano runs

Why we like it

Expected to face 48 balls at number 3
Opportunity drives the projection: 30.1 runs on average, 80 in the top 10% of simulations.
Recent form 40.4 runs an inningsLOW sample
Weighted over the last 0 innings at a strike rate of 75.8.

Risks

Against paceMEDIUM sample
100 runs off 147 balls in 10 ODI matches — strike rate 68.0 vs 76.2 overall, too few dismissals for a meaningful average.
Only 80% likely to play
Taken from how often the player has been selected in this team's recent XIs; the projection is already scaled down for it.

M Mupachikwa runs

Why we like it

Expected to face 53 balls at number 2
Opportunity drives the projection: 26.7 runs on average, 62 in the top 10% of simulations.
Recent form 30.5 runs an inningsLOW sample
Weighted over the last 0 innings at a strike rate of 61.3.

H Deol runs

Why we like it

Expected to face 50 balls at number 5
Opportunity drives the projection: 23.9 runs on average, 64 in the top 10% of simulations.
Recent form 30.3 runs an inningsLOW sample
Weighted over the last 1 innings at a strike rate of 74.8.
Against paceMEDIUM sample
243 runs off 267 balls in 19 ODI matches — strike rate 91.0 vs 77.1 overall, average 40.5 vs 35.6 overall.

Risks

Only 84% likely to play
Taken from how often the player has been selected in this team's recent XIs; the projection is already scaled down for it.

H Kaur runs

Why we like it

Expected to face 40 balls at number 6
Opportunity drives the projection: 17.7 runs on average, 54 in the top 10% of simulations.
Recent form 38.8 runs an inningsLOW sample
Weighted over the last 3 innings at a strike rate of 83.8.
Against paceHIGH sample
530 runs off 545 balls in 39 ODI matches — strike rate 97.2 vs 87.1 overall, average 88.3 vs 43.5 overall.

Risks

Against spinHIGH sample
982 runs off 1184 balls in 48 ODI matches — strike rate 82.9 vs 87.1 overall, average 33.9 vs 43.5 overall.

How these projections are built

The model simulates the match ball by ball from historical delivery data: expected line-ups and batting positions, opposition bowling attack, venue scoring and format baselines. Each figure is the output of those simulations, never an estimate written by hand. Track its settled record on the cricket model performance page, and see the live prices on the cricket betting hub.