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Across 194 WNBA players with at least 10 logged games in turnovers, this page measures 6773 games in total. The median player in that field averages 1.1 turnovers. The line closest to a coin flip sits at 0.5 for the median of the 194 players whose distribution supports one. Half the field is more often over it, half more often under. The deepest sample here is Kaila Charles, with 57 logged games. Of the 106 players whose spread can be measured, 1 cluster tightly around their average, 41 vary moderately and 64 swing widely. 194 of them have at least three games home and away, so the venue gap below is measured rather than assumed. Every figure is counted from official box scores and describes games already played. It is not a projection, and on its own it says nothing about whether any price on offer is worth taking.
Each player's own most balanced threshold: the half-point line he has finished above in closest to half his logged games, measured from his distribution rather than read off a book. Deepest sample first, not best to worst. The 40 deepest samples of 194 measured; the figures above this table are over all 194.
| Player | Club | Logged games | Average | Balanced line | Hit rate there |
|---|---|---|---|---|---|
| Kaila Charles | Valkyries | 57 | 1.0 turnovers | Over 0.5 | 39 of 57 (68%) |
| Kaitlyn Chen | Valkyries | 57 | 1.1 turnovers | Over 0.5 | 37 of 57 (65%) |
| Kayla Thornton | Valkyries | 57 | 0.6 turnovers | Over 0.5 | 23 of 57 (40%) |
| Veronica Burton | Valkyries | 57 | 1.5 turnovers | Over 1.5 | 24 of 57 (42%) |
| Isobel Borlase | Dream | 56 | 0.7 turnovers | Over 0.5 | 29 of 56 (52%) |
| Kiah Stokes | Valkyries | 56 | 0.6 turnovers | Over 0.5 | 25 of 56 (45%) |
| Angel Reese | Dream | 55 | 3.1 turnovers | Over 2.5 | 34 of 55 (62%) |
| Jordin Canada | Dream | 55 | 2.5 turnovers | Over 2.5 | 24 of 55 (44%) |
| Naz Hillmon | Dream | 55 | 1.3 turnovers | Over 1.5 | 23 of 55 (42%) |
| Rhyne Howard | Dream | 55 | 1.4 turnovers | Over 1.5 | 24 of 55 (44%) |
| Tiffany Hayes | Valkyries | 55 | 1.3 turnovers | Over 1.5 | 19 of 55 (35%) |
| Allisha Gray | Dream | 54 | 1.4 turnovers | Over 1.5 | 21 of 54 (39%) |
| DeWanna Bonner | Dream | 54 | 0.8 turnovers | Over 0.5 | 34 of 54 (63%) |
| Gabby Williams | Valkyries | 52 | 1.6 turnovers | Over 1.5 | 24 of 52 (46%) |
| Madina Okot | Dream | 51 | 1.0 turnovers | Over 0.5 | 34 of 51 (67%) |
| Cecilia Zandalasini | Valkyries | 50 | 1.3 turnovers | Over 1.5 | 20 of 50 (40%) |
| Janelle Salaun | Valkyries | 50 | 0.7 turnovers | Over 0.5 | 24 of 50 (48%) |
| Chelsea Gray | Aces | 49 | 2.9 turnovers | Over 2.5 | 27 of 49 (55%) |
| Han Xu | Liberty | 49 | 0.5 turnovers | Over 0.5 | 22 of 49 (45%) |
| Jackie Young | Aces | 49 | 2.5 turnovers | Over 2.5 | 21 of 49 (43%) |
| Pauline Astier | Liberty | 49 | 1.7 turnovers | Over 1.5 | 28 of 49 (57%) |
| Jonquel Jones | Liberty | 48 | 2.2 turnovers | Over 2.5 | 18 of 48 (38%) |
| Stephanie Talbot | Aces | 48 | 0.7 turnovers | Over 0.5 | 25 of 48 (52%) |
| A'ja Wilson | Aces | 47 | 2.3 turnovers | Over 2.5 | 21 of 47 (45%) |
| Arike Ogunbowale | Wings | 47 | 1.7 turnovers | Over 1.5 | 22 of 47 (47%) |
| Breanna Stewart | Liberty | 47 | 2.0 turnovers | Over 1.5 | 28 of 47 (60%) |
| Brianna Turner | Aces | 47 | 0.6 turnovers | Over 0.5 | 20 of 47 (43%) |
| Jewell Loyd | Aces | 47 | 0.9 turnovers | Over 0.5 | 30 of 47 (64%) |
| Kelsey Mitchell | Fever | 47 | 1.6 turnovers | Over 1.5 | 26 of 47 (55%) |
| Lexie Hull | Fever | 47 | 0.7 turnovers | Over 0.5 | 26 of 47 (55%) |
| Georgia Amoore | Mystics | 46 | 2.0 turnovers | Over 1.5 | 29 of 46 (63%) |
| Kayla McBride | Lynx | 46 | 1.3 turnovers | Over 1.5 | 16 of 46 (35%) |
| Lauren Betts | Mystics | 46 | 1.0 turnovers | Over 0.5 | 27 of 46 (59%) |
| Makayla Timpson | Fever | 46 | 0.8 turnovers | Over 0.5 | 25 of 46 (54%) |
| Marine Johannes | Liberty | 46 | 1.5 turnovers | Over 1.5 | 20 of 46 (43%) |
| Maya Caldwell | Lynx | 46 | 0.7 turnovers | Over 0.5 | 25 of 46 (54%) |
| Monique Billings | Fever | 46 | 0.8 turnovers | Over 0.5 | 23 of 46 (50%) |
| Natasha Howard | Lynx | 46 | 2.5 turnovers | Over 2.5 | 19 of 46 (41%) |
| Rebekah Gardner | Liberty | 46 | 0.6 turnovers | Over 0.5 | 20 of 46 (43%) |
| Sophie Cunningham | Fever | 46 | 0.7 turnovers | Over 0.5 | 21 of 46 (46%) |
Standard deviation in the market’s own units, and the same figure as a share of the player’s average so it means the same thing in points as in passing yards. A narrow spread means the average is close to what a typical game actually looked like; it says nothing about what the next one will look like.
| Player | Club | Logged games | Average | Standard deviation | Spread as % of average |
|---|---|---|---|---|---|
| Caitlin Clark | Fever | 43 | 4.7 turnovers | 1.6 | 34% |
| Brittney Griner | Sun | 18 | 1.8 turnovers | 0.7 | 40% |
| Alyssa Thomas | Mercury | 42 | 3.5 turnovers | 1.4 | 41% |
| Marina Mabrey | Tempo | 32 | 3.1 turnovers | 1.3 | 42% |
| Teja Oblak | Fire | 29 | 2.5 turnovers | 1.1 | 43% |
| Kelsey Plum | Mercury | 17 | 2.8 turnovers | 1.3 | 47% |
| Dominique Malonga | Storm | 35 | 2.7 turnovers | 1.4 | 52% |
| Angel Reese | Dream | 55 | 3.1 turnovers | 1.6 | 53% |
| Rae Burrell | Sparks | 43 | 2.4 turnovers | 1.3 | 53% |
| Carla Leite | Fire | 39 | 3.3 turnovers | 1.8 | 53% |
The other end of the same measurement. A wide spread means the average describes the middle of a broad range rather than a typical night, which is worth knowing before reading anything into that average.
| Player | Club | Logged games | Average | Standard deviation | Spread as % of average |
|---|---|---|---|---|---|
| Lexie Brown | Storm | 10 | 1.0 turnovers | 1.3 | 134% |
| Holly Winterburn | Fire | 29 | 1.1 turnovers | 1.2 | 115% |
| Alicia Florez | Mystics | 34 | 1.2 turnovers | 1.3 | 114% |
| Ezi Magbegor | Storm | 14 | 1.1 turnovers | 1.2 | 114% |
| Gabriela Jaquez | Sky | 39 | 1.1 turnovers | 1.2 | 110% |
| Betnijah Laney-Hamilton | Mystics | 32 | 1.1 turnovers | 1.2 | 108% |
| Olivia Nelson-Ododa | Sun | 33 | 1.6 turnovers | 1.7 | 107% |
| Azzi Fudd | Wings | 30 | 1.1 turnovers | 1.2 | 106% |
| Cotie McMahon | Mystics | 44 | 1.0 turnovers | 1.1 | 105% |
| Myisha Hines-Allen | Fever | 40 | 1.2 turnovers | 1.2 | 104% |
Both averages are over the games actually played at each venue, and the game count for each is in the cell beside it. A gap measured over a handful of games either way is one you cannot distinguish from noise. This table prints the denominators so you can tell which is which. Three games a side is the floor for appearing here at all.
| Player | Club | Home (games) | Away (games) | Gap |
|---|---|---|---|---|
| Jordan Harrison | Fire | 3.3 (8) | 0.8 (8) | 2.5 at home |
| DiJonai Carrington | Sky | 0.3 (7) | 1.8 (4) | 1.5 away |
| Ezi Magbegor | Storm | 1.7 (6) | 0.6 (8) | 1.1 at home |
| Aliyah Boston | Fever | 2.6 (21) | 1.7 (22) | 0.9 at home |
| Napheesa Collier | Lynx | 2.7 (9) | 1.8 (9) | 0.9 at home |
| Tonie Morgan | Sparks | 1.3 (7) | 2.2 (5) | 0.9 away |
| Leonie Fiebich | Liberty | 1.4 (14) | 2.3 (12) | 0.9 away |
| Aaliyah Edwards | Sun | 1.4 (17) | 2.2 (13) | 0.8 away |
| Zia Cooke | Storm | 1.5 (21) | 0.7 (20) | 0.8 at home |
| Natisha Hiedeman | Storm | 1.8 (19) | 2.6 (21) | 0.8 away |
Last five logged games against the full log, reported only where the gap is more than a tenth of the player’s own average. It is a description of five games that have been played and it is not a forecast of the sixth.
| Player | Club | Last 5 | Full log | Difference |
|---|---|---|---|---|
| Gianna Kneepkens | Sun | 1.2 turnovers | 0.3 over 36 | 260% above |
| Aaliyah Nye | Sparks | 0.4 turnovers | 0.1 over 20 | 167% above |
| Maddy Siegrist | Wings | 0.8 turnovers | 0.3 over 44 | 135% above |
| Kiah Stokes | Valkyries | 1.4 turnovers | 0.6 over 56 | 124% above |
| Nyadiew Puoch | Fire | 1.4 turnovers | 0.6 over 42 | 118% above |
| Antonia Delaere | Lynx | 1.0 turnovers | 0.5 over 40 | 111% above |
| Kiki Rice | Tempo | 3.6 turnovers | 1.8 over 28 | 102% above |
| Karlie Samuelson | Fire | 0.8 turnovers | 0.4 over 32 | 97% above |
| Kate Martin | Sky | 0.4 turnovers | 0.2 over 19 | 90% above |
| Makayla Timpson | Fever | 1.6 turnovers | 0.8 over 46 | 89% above |
The same comparison in the other direction, and the same caveat: five games is five games, and a run below a baseline is as often noise as it is a change.
| Player | Club | Last 5 | Full log | Difference |
|---|---|---|---|---|
| Angela Dugalic | Mystics | 0.0 turnovers | 0.6 over 40 | 100% below |
| Kierstan Bell | Aces | 0.0 turnovers | 0.4 over 32 | 100% below |
| Shatori Walker-Kimbrough | Dream | 0.0 turnovers | 0.2 over 19 | 100% below |
| Grace VanSlooten | Fever | 0.0 turnovers | 0.2 over 16 | 100% below |
| Rori Harmon | Mystics | 0.0 turnovers | 0.8 over 15 | 100% below |
| Liatu King | Lynx | 0.0 turnovers | 0.4 over 12 | 100% below |
| Betnijah Laney-Hamilton | Mystics | 0.2 turnovers | 1.1 over 32 | 82% below |
| Kaitlyn Chen | Valkyries | 0.2 turnovers | 1.1 over 57 | 81% below |
| Ariel Atkins | Sparks | 0.4 turnovers | 1.4 over 42 | 72% below |
| Eliska Hamzova | Lynx | 0.2 turnovers | 0.7 over 15 | 70% below |
At the half-point threshold each player has finished above in about half their logged games. It is measured per player from their own distribution rather than read off a sportsbook, which is why it is on this page at all: a posted line moves through the day and this page is rebuilt once, so a number taken from a book would be wrong by morning.
No. Nothing here is ordered by how good a bet it is, and no price is quoted anywhere on the page. The tables are ordered by sample depth, by spread and by the size of a measured gap, because those are the things a game log can actually establish.
The count is printed beside every one of them. A player only appears once they have ten or more logged games in the market, and games they did not play are absent rather than counted as zero, so an average describes games they were actually in.
No. A trend here is a comparison between a player’s last five logged games and their full log, and it is reported only when the gap is large enough to be worth naming. It is a description of what has happened, and history is not a forecast.
What these tables are measuring, explained: how to read a hit rate and what its denominator hides, what an over/under line is and what it is not.
WNBA player props and live odds
Odds and lines come from licensed data feeds and refresh on a schedule; hit rates and grades are Parlay Builder’s own calculations.