We’ll have to wait and see if this game is a sign of things to come. GW was very Francis-heavy in this game. She accounted for a full 15% of the Bulldogs play shares (her weighted percentage was 20.4% which discounts things like draws).

As with many of these early season statistical oddities, this one likely won’t last, but one thing that stands out for Hernandez so far is her shooting. She’s scored 10 goals on just 14 shots, every one of which was on goal. It’s good news for an offense when your top share offensive player is shooting lights out.

Think of it this way, against PSU, Spilis put up a point roughly once for every 10 RU possessions. Against JHU, it was more like one point for every 6 Scarlet Knights possessions.

These EGA lists are not pace-adjusted, so teams who play faster tend to land more players on them. The fact that Spilis earned the #3 spot in a fairly slow-paced game make it all the more impressive.

Rippley took more shots, had more shots on goal, scored more points, had more GBs and put up more total EGA against ECU than she did combined in the team’s first two games. Her raw play-share in this one was 15.2% after being just 8.6% and 3.5% in the team’s first two games.

An interesting note on the Wildcats’ offense. Against Duke, they faced a -14 possession margin after winning just 31% of the draws. In that game, they also played at their slowest pace of the year (46.5s per possession). They’ve sped up since then, clocking an average possession of just 33.1 seconds against ECU.

Different year, different story against SDSU. Adams weighted play share in this one was 14.7%, nearly doubling her average last year. She also took 12 shots after averaging just 3.5 per game last year. Sometimes, you’ll see a player’s turnover rate increase when they take on a heavier load. Usually, the best you can hope for is that it stays flat.

For Adams though (one game caveat aside), her share-adjusted turnover rate in this one was just .18 after finishing ’20 with a .53 mark. If she can keep that up, the efficiency bump that ASU saw in this one may be quite durable.

And Parrella has been a big part of that, clocking a team-leading 21.0% weighted play-share so far. One warning sign to watch for her though is her shooting. In 2020, her shooting percentage was almost exactly what you’d expect given the profile of the shots she took. She was slightly better than a league average shooter. In 2 games so far this year, she’s scored almost 2.5 goals less than you’d expect given the profile of the shots she’s taken.

It may very well be early-season rust that will be falling off here shortly, but if she can get back to being roughly league-average, that’ll give this offense another boost.

’17: 37.9%
’18: 37.5%
’19: 45.7%
’20: 59.3%
’21: 66.7%

And she is a great example of why we need to look at expected shooting percentages, which allow us to compare how many goals a player scores compared to an average shooter. Bottom line, the chances she’s getting haven’t improved, she’s just been successful in canning more of them. This is the number of “excess-goals” she’s scored per attempt over her Lehigh career.

’17: -.00
’18: -.02
’19: +.08
’20: +.19
’21: +.25

A player’s shooting percentage can go up simply because they are getting better chances; for Dickey, there is real evidence that she’s just increased her shooting skill over the years.

Obviously, a lot of the credit goes to Skalstad, who won 12 draws in this one to go along with her 5 goals on 8 shots.

Her shots/game is up this year, in part because she’s done a better job on the ball security front.