NWSL Fantasy League: Using Analytics to Pick a Squad

I normally don’t participate in fantasy sports because they involve me rooting for weird things like Liverpool keeping a clean sheet while Daley Blind scores a goal and Yaya Toure gets a couple of assists. I can’t keep it straight, and it takes most of the enjoyment out of the game for me. However, NWSLFL has been fun for me and it’s forced me to immerse myself a little more in the league and learn more about all the players which is a good thing for someone trying to do analytics.1

I wanted to share my thought processes for my third week’s success. Weeks 1 and 2 were pretty disastrous, but Week 3 I scored fairly well.  I acknowledge there’s a lot of luck in this, but I do think I’ve improved my process and I figured I’d share it with people and maybe they can use it to do well, or at least join me in failure if this turns out to be a bad strategy long-term.

Step 1: Find The Most Likely Winners

NWSL Week 4 Predictions

I use my prediction model (OHAI) to see which teams are most likely to win, although I’m not 100% confident in the model so I also apply a logic test to it. This week, I’m looking at FC Kansas City to beat the Houston Dash or the Spirit over the Thorns. This is where I build my defense from, and usually where I pick my goalkeeper. I do like Nicole Barnhart, so I’ll probably pick her as my starting goalkeeper instead of Hope Solo (last week’s GK). I’ll pick a couple defenders from Kansas City, and a couple from the Spirit.

Step 2: Find Teams Who Are Under/Overachieving Expected Goals

My Expected Goals (xG) model predicts how many goals a team should score given the types of shots they have taken, and then I compare that to how many goals they’ve scored. If they’ve scored far more goals than I anticipated, they’re possibly due to have an off day. If they’ve scored less, they’re possibly due to have a good day. Last week the Houston Dash were *way* above the line, meaning that they’d scored far more goals than you’d expect given their shots. So I might pick against them and avoid their strikers – I could have even picked some midfielders from their opponents. I also might look at a team who has been expected to score a lot of goals but has come short and pick some of their attacking players. The WNY Flash look like they might be due for some goals, Seattle might be in for a little dry spell here.

NWSL Week 3 xG v Actual

Then I look at expected goals allowed to see who’s underachieving/overachieving there. The Spirit have been allowing fewer goals than expected, as have Sky Blue and FC Kansas City. That would mean a couple of things: they’re either due to allow some goals or their goalkeepers are extra good and are preventing goals from going in. I’ve watched Nicole Barnhart and she’s been fairly heroic in goal, so she might continue the pattern. Meanwhile, Orlando has let in more goals than expected so they might be due for some opponents to hit the post.

NWSL Week 3 xGA v Actual

Step 3: Picking the Rest

I generally pick my USWNT designated players for the midfield – Tobin Heath and Christen Press always seem like safe bets to do good things. I like Kim Little right now because she’ll likely step up given all the injuries in Seattle. I also captain my goalkeeper because the top scorers usually seem to be goalkeepers (saves + clean sheet + winning bonus are a good combination if you can get it right). And I pick Kealia Ohai because she’s the namesake for my model so why not?

I haven’t picked my team this week, but this is the process. I like the new procedure, and I got super lucky last weekend with just about all my players scoring significant points. I missed Diana Matheson’s hat trick, but I think every player on my team had a goal or an assist last week, and all my defenders won or kept a clean sheet. Hope Solo didn’t face a ton of shots which hurt, but she won with a clean sheet so that was as much as I could have hoped for. Hopefully people can build on this, and I’d love to hear your refinements on the strategy!

  1. Subject matter is dramatically underrated in a lot of analytics exercises, but that’s a story for another day

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