How WNBA turnovers actually behave

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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.

Measured as of 2026-11-15, rebuilt daily from official box scores. Window: 2026-05-08 to 2026-10-31. No prices appear on this page.

6,773 logged player-games from 353 unique games. A player-game is one player in one game, so a single game counts once for every player it logged.

Where the coin-flip line sits in WNBA turnovers

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.

WNBA turnovers: measured field
PlayerClubLogged gamesAverageBalanced lineHit rate there
Kaila CharlesValkyries571.0 turnoversOver 0.539 of 57 (68%)
Kaitlyn ChenValkyries571.1 turnoversOver 0.537 of 57 (65%)
Kayla ThorntonValkyries570.6 turnoversOver 0.523 of 57 (40%)
Veronica BurtonValkyries571.5 turnoversOver 1.524 of 57 (42%)
Isobel BorlaseDream560.7 turnoversOver 0.529 of 56 (52%)
Kiah StokesValkyries560.6 turnoversOver 0.525 of 56 (45%)
Angel ReeseDream553.1 turnoversOver 2.534 of 55 (62%)
Jordin CanadaDream552.5 turnoversOver 2.524 of 55 (44%)
Naz HillmonDream551.3 turnoversOver 1.523 of 55 (42%)
Rhyne HowardDream551.4 turnoversOver 1.524 of 55 (44%)
Tiffany HayesValkyries551.3 turnoversOver 1.519 of 55 (35%)
Allisha GrayDream541.4 turnoversOver 1.521 of 54 (39%)
DeWanna BonnerDream540.8 turnoversOver 0.534 of 54 (63%)
Gabby WilliamsValkyries521.6 turnoversOver 1.524 of 52 (46%)
Madina OkotDream511.0 turnoversOver 0.534 of 51 (67%)
Cecilia ZandalasiniValkyries501.3 turnoversOver 1.520 of 50 (40%)
Janelle SalaunValkyries500.7 turnoversOver 0.524 of 50 (48%)
Chelsea GrayAces492.9 turnoversOver 2.527 of 49 (55%)
Han XuLiberty490.5 turnoversOver 0.522 of 49 (45%)
Jackie YoungAces492.5 turnoversOver 2.521 of 49 (43%)
Pauline AstierLiberty491.7 turnoversOver 1.528 of 49 (57%)
Jonquel JonesLiberty482.2 turnoversOver 2.518 of 48 (38%)
Stephanie TalbotAces480.7 turnoversOver 0.525 of 48 (52%)
A'ja WilsonAces472.3 turnoversOver 2.521 of 47 (45%)
Arike OgunbowaleWings471.7 turnoversOver 1.522 of 47 (47%)
Breanna StewartLiberty472.0 turnoversOver 1.528 of 47 (60%)
Brianna TurnerAces470.6 turnoversOver 0.520 of 47 (43%)
Jewell LoydAces470.9 turnoversOver 0.530 of 47 (64%)
Kelsey MitchellFever471.6 turnoversOver 1.526 of 47 (55%)
Lexie HullFever470.7 turnoversOver 0.526 of 47 (55%)
Georgia AmooreMystics462.0 turnoversOver 1.529 of 46 (63%)
Kayla McBrideLynx461.3 turnoversOver 1.516 of 46 (35%)
Lauren BettsMystics461.0 turnoversOver 0.527 of 46 (59%)
Makayla TimpsonFever460.8 turnoversOver 0.525 of 46 (54%)
Marine JohannesLiberty461.5 turnoversOver 1.520 of 46 (43%)
Maya CaldwellLynx460.7 turnoversOver 0.525 of 46 (54%)
Monique BillingsFever460.8 turnoversOver 0.523 of 46 (50%)
Natasha HowardLynx462.5 turnoversOver 2.519 of 46 (41%)
Rebekah GardnerLiberty460.6 turnoversOver 0.520 of 46 (43%)
Sophie CunninghamFever460.7 turnoversOver 0.521 of 46 (46%)

The steadiest WNBA turnovers records

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.

The steadiest WNBA turnovers records
PlayerClubLogged gamesAverageStandard deviationSpread as % of average
Caitlin ClarkFever434.7 turnovers1.634%
Brittney GrinerSun181.8 turnovers0.740%
Alyssa ThomasMercury423.5 turnovers1.441%
Marina MabreyTempo323.1 turnovers1.342%
Teja OblakFire292.5 turnovers1.143%
Kelsey PlumMercury172.8 turnovers1.347%
Dominique MalongaStorm352.7 turnovers1.452%
Angel ReeseDream553.1 turnovers1.653%
Rae BurrellSparks432.4 turnovers1.353%
Carla LeiteFire393.3 turnovers1.853%

The widest WNBA turnovers swings

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.

The widest WNBA turnovers swings
PlayerClubLogged gamesAverageStandard deviationSpread as % of average
Lexie BrownStorm101.0 turnovers1.3134%
Holly WinterburnFire291.1 turnovers1.2115%
Alicia FlorezMystics341.2 turnovers1.3114%
Ezi MagbegorStorm141.1 turnovers1.2114%
Gabriela JaquezSky391.1 turnovers1.2110%
Betnijah Laney-HamiltonMystics321.1 turnovers1.2108%
Olivia Nelson-OdodaSun331.6 turnovers1.7107%
Azzi FuddWings301.1 turnovers1.2106%
Cotie McMahonMystics441.0 turnovers1.1105%
Myisha Hines-AllenFever401.2 turnovers1.2104%

The widest home and away gaps in WNBA turnovers

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.

WNBA turnovers: venue gaps
PlayerClubHome (games)Away (games)Gap
Jordan HarrisonFire3.3 (8)0.8 (8)2.5 at home
DiJonai CarringtonSky0.3 (7)1.8 (4)1.5 away
Ezi MagbegorStorm1.7 (6)0.6 (8)1.1 at home
Aliyah BostonFever2.6 (21)1.7 (22)0.9 at home
Napheesa CollierLynx2.7 (9)1.8 (9)0.9 at home
Tonie MorganSparks1.3 (7)2.2 (5)0.9 away
Leonie FiebichLiberty1.4 (14)2.3 (12)0.9 away
Aaliyah EdwardsSun1.4 (17)2.2 (13)0.8 away
Zia CookeStorm1.5 (21)0.7 (20)0.8 at home
Natisha HiedemanStorm1.8 (19)2.6 (21)0.8 away

Ahead of their own baseline over the last five

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.

Ahead of their own baseline over the last five
PlayerClubLast 5Full logDifference
Gianna KneepkensSun1.2 turnovers0.3 over 36260% above
Aaliyah NyeSparks0.4 turnovers0.1 over 20167% above
Maddy SiegristWings0.8 turnovers0.3 over 44135% above
Kiah StokesValkyries1.4 turnovers0.6 over 56124% above
Nyadiew PuochFire1.4 turnovers0.6 over 42118% above
Antonia DelaereLynx1.0 turnovers0.5 over 40111% above
Kiki RiceTempo3.6 turnovers1.8 over 28102% above
Karlie SamuelsonFire0.8 turnovers0.4 over 3297% above
Kate MartinSky0.4 turnovers0.2 over 1990% above
Makayla TimpsonFever1.6 turnovers0.8 over 4689% above

Behind their own baseline over the last five

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.

Behind their own baseline over the last five
PlayerClubLast 5Full logDifference
Angela DugalicMystics0.0 turnovers0.6 over 40100% below
Kierstan BellAces0.0 turnovers0.4 over 32100% below
Shatori Walker-KimbroughDream0.0 turnovers0.2 over 19100% below
Grace VanSlootenFever0.0 turnovers0.2 over 16100% below
Rori HarmonMystics0.0 turnovers0.8 over 15100% below
Liatu KingLynx0.0 turnovers0.4 over 12100% below
Betnijah Laney-HamiltonMystics0.2 turnovers1.1 over 3282% below
Kaitlyn ChenValkyries0.2 turnovers1.1 over 5781% below
Ariel AtkinsSparks0.4 turnovers1.4 over 4272% below
Eliska HamzovaLynx0.2 turnovers0.7 over 1570% below

Common questions

Where does the WNBA turnovers coin-flip line sit?

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.

Is this a ranking of the best WNBA turnovers props?

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.

How many games are behind each turnovers figure?

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.

Does a turnovers trend on this page predict the next game?

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.