Expected goals is a hockey metric, often considered an advanced statistic used in analytics to help predict scoring. Expected goals, in short, is the chance that an unblocked shot becomes a goal. 

What this metric tells hockey fans, is which players are getting to the best scoring positions more often and getting unblocked shots to the net from those areas. A score has been assigned to the various areas on the ice based on how likely it is for a shot from a specific area is to be scored. When a player takes a shot from that area successfully, it counts toward their xG tally. The metric also considers the best shooting angles, and type of shot taken (Eg. Wrist Shot vs. Slapshot).

It also allows coaches and scouts to see which players are scoring more than expected, and less than expected. A player who consistently, year over year scores more than expected is likely a very skilled shooter. They would not only get to high potency shooting areas, but couple that with stronger and more accurate shots. Short term it could be luck, but game over game and year over year, the data speaks.

Conversely, a player who regularly under performs their expected goals may be a weaker shooter, or as it may be, regularly faces better goaltenders. Whether they underperform or over-perform xG, the statistic has proven reliable for offensive production, and is a often a better indication of offensive potential compared to actual output based on a number of factors.

Here’s a look at the top players for expected goals per game, net expected goals per game, and total expected goals versus actual total goals for eligible prospects from the NCAA ahead of the 2026 PWHL Draft. 

xG (per game) of NCAA Prospects for 2025 PWHL Draft

Originally a soccer metric, hockey co-opted the advanced statistic and applied it to the ice. Throughout a game, or season, a player accumulates expected goals totals. For example, a shot from the slot, ten feet from the net might accumulated a 0.3 expected goals (xG). That means that based on statistical models, a shot from that location should score 30% of the time. A shot from the blueline conversely, might only have an expected goals (xG) total of 0.05, meaning only five percent of shots from that location can be expected to score. Not only do these models factor in location, they also consider angle, and type of shot. For example, a slap shot from the left wall near the blueline will have a different value than a write shot from the same location.

Here’s a look at the top 25 PWHL Draft eligible players in the expected goals (xG) per game category from the 2025-26 NCAA season.

1. Abbey Murphy, F, Minnesota - 0.99
2. Sloane Matthews, F, Ohio State - 0.77
3. Carina DiAntonio, F, Yale - 0.75
4. Issy Wunder, F, Princeton - 0.74
5. Lacey Eden, F, Wisconsin - 0.66
6. Kirsten Simms, F, Wisconsin - 0.64
7. Brooklyn Schneiderhan, F, Saint Anslem - 0.62
8. Elyssa Biederman, F, Colgate - 0.61
9. Jordan Ray, F, Yale - 0.56
10. Jade Iginla, F, Brown - 0.55
11. Tessa Janecke, F, Penn State - 0.50
12. Lily Shannon, F, Northeastern - 0.50
13. Sena Catterall, F, Clarkson - 0.50
14. Thea Johansson, F, Minnesota-Duluth - 0.49
15. Reichen Kirchmair, F, Providence - 0.47
16. Emerson Jarvis, F, Quinnipiac - 0.45
17. Laurence Frenette, F, Quinnipiac - 0.43
18. Lilli Welcke, F, Boston University - 0.43
19. Rhea Hicks, F, Clarkson - 0.43
20. Alyson Hush, F, New Hampshire - 0.43
21. Lara Beecher, F, Clarkson - 0.43
22. Emerson O'Leary, F, Princeton - 0.42
23. India McDadi, F, Brown - 0.41
24. Caroline Harvey, D, Wisconsin - 0.40
25. Megan Woodworth, F, UConn - 0.40

xG Net of NCAA Prospects for 2025 PWHL Draft

To look at a player’s ability to drive high quality scoring chances, and suppress high quality scoring while they’re on the ice, we can look at Net Expected Goals (Net xG). Net xG looks at the expected goals for a player and their team while they are on the ice, versus the expected goals of their opponents while they’re on the ice. It looks at the likelihood a goal is scored for, or against, while a specific player is on the ice.

Here’s a look at the top 25 PWHL Draft eligible players in the net expected goals (Net xG) per game category from the 2025-26 NCAA season.

1. Caroline Harvey, D, Wisconsin - 1.28
2. Kirsten Simms, F, Wisconsin - 1.22
3. Sloane Matthews, F, Ohio State - 1.21
4. Lacey Eden, F, Wisconsin - 1.21
5. Issy Wunder, F, Princeton - 1.05
6. Emma Peschel, D, Ohio State - 1.05
7. Abbey Murphy, F, Minnesota - 1.01
8. Gracie Gilkyson, D, Yale - 0.99
9. Carina DiAntonio, F, Yale - 0.93
10. Laila Edwards, F/D, Wisconsin - 0.93
11. Jordan Ray, F, Yale - 0.90
12. Josefin Bouveng, F, Minnesota - 0.88
13. Emerson O'Leary, F, Princeton - 0.77
14. Katelyn Roberts, F, Penn State - 0.73
15. Sydney Morrow, D, Minnesota - 0.73
16. Lara Beecher, F, Clarkson - 0.66
17. Maddy Christian, F, Penn State - 0.66
18. Vivian Jungels, D, Wisconsin - 0.65
19. Kendall Butze, D, Penn State - 0.64
20. Sara Swiderski, D, Ohio State - 0.62
21. Sena Catterall, F, Clarkson - 0.60
22. Naomi Boucher, F, Yale - 0.56
23. Rhea Hicks, F, Clarkson - 0.48
24. Nelli Laitinen, D, Minnesota - 0.48
25. Tessa Janecke, F, Penn State - 0.47

Goals Above Expected of NCAA Prospects for 2025 PWHL Draft

This metric looks at the actual number of goals scored by players with the highest xG and Net xG (from above). Listed are their total actual goals, total xG, and the +/- differential between the numbers. Players are listed ranked based on actual goals scored this season. (Goals Scored - Total xG = Difference)

1. Abbey Murphy, F, Minnesota: 40 - 30.8 = -9.2
2. Lacey Eden, F, Wisconsin: 27 - 25.8 = +1.2
3. Issy Wunder, F, Princeton: 27 - 25 = -2
4. Tessa Janecke, F, Penn State: 27 - 16.7 = +9.3
5. Carina DiAntonio, F, Yale: 26 - 27.1 = -1.1
6. Kirsten Simms, F, Wisconsin: 23 - 18.4 = +4.6
7. Brooklyn Schneiderhan, F, Saint Anslem: 21 - 19.9 = +1.1
8. Sloane Matthews, F, Ohio State: 20 - 31.5 = -11.5
9. Lily Shannon, F, Northeastern: 19 - 19.7 = -0.7
10. Maddy Christian, F, Penn State: 19 - 14.9 = +4.1
11. Sydney Healey, F, Boston University: 18 - 12.2 = +5.8
12. Caroline Harvey, D, Wisconsin: 18 - 12.4 = +5.6
13. Alexis Petford, F, Colgate: 18 - 12 = +6
14. Elyssa Biederman, F, Colgate: 17 - 21.9 = -4.9
15. Jordan Ray, F, Yale: 17 - 20.1 = +3.1
16. Emerson Jarvis, F, Quinnipiac = 17 - 19 = -2
17. Jade Iginla, F, Brown: 17 - 17.6 = -0.6
18. Katelyn Roberts, F, Penn State: 16 - 12.4 = +3.6
19. Sena Catterall, F, Clarkson: 15 - 17.2 = -1.8
20. Josefin Bouveng, F, Minneota: 15 - 11.8 = +3.2
21. Reichen Kirchmair, F, Providence: 14 - 16 = +2
22. Megan Woodworth, F, UConn: 14 - 14.9 = -0.9
23. Alexia Moreau, F, Union College: 13 - 10.8 = +2.2
24. Lara Beecher, F, Clarkson: 12 - 14.6 = -2.6
25. Thea Johansson, F, Minneota-Duluth: 12 - 12.3 = -0.3

**All data comes from InStat