Simulation Results: South African Women's Open

Player	Win %	TT Odds	Best Odds	Kelly	Result	P/L	E/W Play
Agathe Laisne	6.51%	15	16	0.30%	29	-0.3	Spreadex: 15/1, 5pl, 1/4
Agathe Sauzon	0.12%	833	451		74
Aine Donegan	0.14%	714	161		37
Alessandra Fanali	1.78%	56	31		7
Alessia Nobilio	0.60%	167	176		50
Alexandra Forsterling	3.57%	28	23		10
Alexandra Swayne	0.07%	1429	501		57
Alice Hewson	2.24%	45	34		15
Amaia Latorre	0.20%	500	351		29
Amalie Leth-Nissen	0.00%	10000	1001		95
Amelia Garvey	4.93%	20	17		29
Amy Lee	0.14%	714	751		15
Amy Taylor	0.26%	385	451		60
Ana Dawson	0.10%	1000	301		100
Ana Pelaez Trivino	0.99%	101	56		24
Andrea Grimberg Lignell	0.02%	5000	501		113
Anna Foster	0.65%	154	111		15
Anna Huang	4.39%	23	21		7
Anna Morgan	1.19%	84	67		4
Anna Zanusso	1.13%	88	101	0.10%	15	-0.1	Spreadex: 100/1, 5pl, 1/4
Annabell Fuller	1.36%	74	67		10
Anne-Charlotte Mora	0.05%	2000	301		80
April Angurasaranee	0.48%	208	61		80
Aunchisa Utama	0.59%	169	176		67
Avani Prashanth	0.35%	286	161		57
Ayako Uehara	0.29%	345	601	0.10%	80	-0.1	Bet365: 600/1, 5pl, 1/4
Blanca Fernandez	0.86%	116	91		74
Bobbi Brown	0.00%	10000	86		87
Brianna Navarrosa	2.43%	41	41		4
Caitlyn Macnab	0.28%	357	101		50
Camille Chevalier	0.99%	101	101		67
Cara Gainer	2.83%	35	31		2
Cara Gorlei	0.05%	2000	351		80
Carolin Kauffmann	0.04%	2500	351		60
Casandra Alexander	10.87%	9.2	5		67
Cecilie Finne-Ipsen	0.03%	3333	1001		19
Celina Sattelkau	0.79%	127	56		24
Charlotte Heath	0.96%	104	111	0.10%	87	-0.1	Spreadex: 110/1, 5pl, 1/4
Charlotte Millard	0.00%	10000			113
Corinne Viden	0.34%	294	226		24
Danielle Du Toit	0.23%	435	86		108
Darcey Harry	1.14%	88	56		34
Diksha Dagar	2.12%	47	51	0.20%	37	-0.2	Bet365: 50/1, 5pl, 1/4
Dorota Zalewska	0.03%	3333	501		44
Dorthea Forbrigd	1.08%	93	71		74
Emma Falcher	0.62%	161	226	0.20%	19	-0.2	Spreadex: 225/1, 5pl, 1/4
Emma Grechi	0.15%	667	501		50
Emma Spitz	1.61%	62	67	0.10%	4	0.48333333333333	Bet365: 66/1, 5pl, 1/4
Esme Hamilton	0.52%	192	176		1
Fernanda Lira	0.06%	1667	351		37
Gabriella Cowley	0.06%	1667	501		74
Gabrielle Venter	0.41%	244	91		59
Gemma Clews	0.33%	303	111		44
Ginnie Ding	0.08%	1250	501		60
Gudrun Bjorgvinsdottir	0.09%	1111	251		100
Hannah Burke	0.16%	625	601		100
Hannah Darling	0.02%	5000	401		117
Hannah Screen	1.07%	93	141	0.40%	19	-0.4	Bet365: 140/1, 5pl, 1/4
Harang Lee	0.07%	1429	501		67
Hitaashee Bakshi	0.22%	455	251		95
Isabella Ferreira	0.00%	10000			117
Jana Melichova	1.05%	95	126	0.30%	100	-0.3	Spreadex: 125/1, 5pl, 1/4
Jess Baker	0.12%	833	401		74
Jordan Rothman	0.00%	10000	201		113
Justice Bosio	0.19%	526	126		24
Kaiyuree Moodley	0.00%	10000	351		87
Kajal Mistry	0.02%	5000	601		112
Kajsa Arwefjall	4.46%	22	23	0.10%	10	-0.1	Bet365: 22/1, 5pl, 1/4
Katharina Muehlbauer	0.18%	556	226		87
Kelsey Bennett	2.63%	38	19		67
Kiera Floyd	0.05%	2000	126		63
Kirsten Rudgeley	3.85%	26	16		34
Klara Davidson Spilkova	0.44%	227	276	0.10%			Bet365: 275/1, 5pl, 1/4
Laura Beveridge	0.40%	250	201		95
Lee-Anne Pace	0.61%	164	56		67
Lily May Humphreys	0.21%	476	351		55
Lisa Coetzer	0.01%	10000	126		65
Lisa Pettersson	1.13%	88	101	0.10%	10	-0.1	Bet365: 100/1, 5pl, 1/4
Lois Lau	0.14%	714	226		44
Lora Assad	0.00%	10000	1001		108
Lorna Mcclymont	0.05%	2000	501		100
Louise Rydqvist	0.01%	10000	601		113
Luca Thompson	0.01%	10000	601		63
Lucie Malchirand	0.00%	10000	301		66
Luna Sobron Galmes	1.64%	61	81	0.40%	29	-0.4	Star Sports: 80/1, 5pl, 1/4
Lydia Hall	0.73%	137	126		29
Maddison Hinson-Tolchard	1.81%	55	56		44
Madelene Stavnar	0.06%	1667	301		19
Maha Haddioui	0.84%	119	201	0.30%	7	-0.3	Spreadex: 200/1, 5pl, 1/4
Maria Parra	0.03%	3333	501		37
Marta Martin	0.07%	1429	351		108
Marta Sanz Barrio	0.06%	1667	601		87
Meghan MacLaren	0.60%	167	81		19
Moa Folke	0.70%	143	86		100
Momoka Kobori	0.99%	101	81		44
Morgane Metraux	4.28%	23	29	0.90%	67	-0.9	Bet365: 28/1, 5pl, 1/4
Nadia Van Der Westhuizen	0.05%	2000	81		95
Nina Pegova	0.00%	10000	351		87
Nobuhle Dlamini	0.10%	1000	301		55
Olivia Mehaffey	0.04%	2500	601		37
Patricia Isabel Schmidt	0.91%	110	111		37
Patricie Mackova	0.41%	244	201		120
Pia Babnik	2.40%	42	34		3
Pranavi Urs	1.09%	92	126	0.30%	24	-0.3	Bet365: 125/1, 5pl, 1/4
Roberta Liti	1.47%	68	91	0.40%	37	-0.4	Bet365: 90/1, 5pl, 1/4
Romy Meekers	0.11%	909	451		80
Sanna Nuutinen	0.02%	5000	601		80
Sara Kouskova	0.43%	233	126		34
Smilla Tarning Soenderby	0.20%	500	161		44
Sofie Kibsgaard Nielsen	0.82%	122	81		10
Symone Henriques	0.00%	10000	751		119
Tandi McCallum	0.00%	10000	1001		100
Tereza Melecka	0.06%	1667	501		108
Thalia Martin	0.09%	1111	501		50
Tina Mazarino	0.99%	101	401	0.70%	50	-0.7	Bet365: 400/1, 5pl, 1/4
Tvesa Malik	0.05%	2000	501		87
Vanessa Knecht	0.63%	159	101		80
Vani Kapoor	0.03%	3333	501		74
Verena Gimmy	0.09%	1111	351		87
Zane Kleynhans	0.03%	3333	276		95

					Result	P/L	E/W Play

Player	Win %	TT Odds	Best Odds	Kelly	Result	P/L	E/W Play
WIN: Morgane Metraux	4.28%	23	29	0.90%	67	-2	Bet365: 28/1, 5pl, 1/4
WIN: Tina Mazarino	0.99%	101	401	0.70%	50	-2	Bet365: 400/1, 5pl, 1/4
WIN: Luna Sobron Galmes	1.64%	61	81	0.40%	29	-2	Star Sports: 80/1, 5pl, 1/4
FRL:
FRL:
FRL:
FRL:
FRL:
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The data in the above table is as follows:

• Player - the players in the field at the time of the latest update
• TT Win - our estimated probability of the player winning this market
• TT Odds - The TT Win value expressed in decimal odds format
• Best Odds - the best odds available on the player to win this market as indicated on Oddschecker
• Kelly - The Kelly criterion as a measure of the 'edge' in each player's odds - the Kelly criterion calculates the optimal percentage of bankroll to bet on a player
• Result - The player's finishing position - this is added when this simulation is archived
• P/L - The profit on the advised play (see 'E/W Play' column) - this is added when this simulation is archived
• E/W Play - The advised each-way play at the Kelly stakes

The Top3 Plays table highlights the three advised each-way plays in the win and FRL markets. Each play is of equal stake with a total of 6pts in both the win and FRL markets.

The TT Win Probabilities displayed above are the result of a Monte Carlo simulation of each round of a tournament, using individual player inputs and a probability distribution. This method is particularly useful in golf given the number of players in a tournament and the complex process within modelling player scores.

To determine the number of replications R, this is typically set at 10 / e², where e is the relative error in the bias assessment (precision measure). With 100,000 replications, e falls to less than 1%, which is the target relative error.

This table will be populated with the simulation results as soon as there are sufficient win odds available to advise plays. The FRL probabilities and advised plays will not appear publication of tee-times for events with split rd1 tee-times.

While TT Win Probabilities will be provided for all markets available via the buttons above the table, each-way plays will only be advised for win and FRL markets.

TT Win Probabilities (including results) are available for all past events via the 'Archives' here.

Full P/L breakdown per market and per event since 2024 is available here.