On October 16 a single DFS late scratch sat in 29.1% of the NHL lineups I had built for a two-game slate. Egor Chinakhov, a $4,100 Penguins winger, was ruled out about 25 minutes before puck drop, and my Frozen Tools set had him in 134 of its 461 lineups. In the real DraftKings contests, 1.6% of entries had him. I have since rebuilt the night without him, checked four other nights, and added a “Check late news” button to my NHL lineup optimizer and the other three sports. The surprise is how little removing him changed.
Continue reading “One Scratched Winger Was in 29% of My NHL DFS Lineups”Tag: NHL
How to Play Fantasy Sports Strategically: My Review
A review of Haugh and Singal’s DFS paper on modeling opponents, and the simulation-based NHL lineup generator I built from it.
Earlier this week I wrote about what an MIT DFS paper taught my NHL lineup optimizer. Read that paper and you quickly run into its follow-up: Martin Haugh (Imperial College) and Raghav Singal (Columbia), How to Play Fantasy Sports Strategically (and Win), later published in Management Science. It builds directly on the MIT work and asks the one question that paper skipped: what is everybody else doing? Their answer is that to play fantasy sports strategically you can’t just maximize your own points. You have to beat a field of lineups you can’t see yet.
I read it for a selfish reason. I have been paper-trading my NHL lineups for a week (I build them before lock, then score them against the real DraftKings fields once the games are over), and after five slates no setup has made money. None of them (that is not a typo). So this post is half review, half lab notebook: what the paper does, what I borrowed, and the different kind of lineup generator I am building along the same lines.
My NHL Lineup Optimizer Now Skips the Solver (Mostly)
My NHL lineup optimizer used to build every lineup the same way: hand the whole problem to a solver and wait. It now skips the solver for most of the work. It lists the stacks that are allowed, scores them, and picks the best one, and it does that for a whole batch on a DraftKings or FanDuel slate in about a tenth of a second. The solver I have always reached for is still in the tool, but as the referee rather than the player.
Yahoo DFS NHL Optimizer: Two Goalies, New Rules
My free NHL lineup optimizer now builds lineups for Yahoo, the third site after DraftKings and FanDuel. I expected a Yahoo DFS NHL optimizer to be a matter of a new file parser and a new roster shape. It was mostly that, except for one line in Yahoo’s roster that broke three of my assumptions: two goalies. This post is about what had to change, which settings would have quietly made every lineup impossible, and the one new rule I added because of it: never repeat the same goalie pair.
What an MIT DFS Paper Taught My NHL Lineup Optimizer
An MIT NHL DFS paper found win probability was the only goalie predictor that mattered. So I rebuilt my NHL lineup optimizer around goalies.
Last week I came across a paper from three MIT operations research people — David Scott Hunter, Juan Pablo Vielma and Tauhid Zaman — called Picking Winners in Daily Fantasy Sports Using Integer Programming. It is about building a set of NHL DFS lineups with a solver, which is exactly what my free NHL lineup optimizer does, so I read it with a pencil in hand. They did not just run simulations: they entered real DraftKings contests and report that “we are able to rank in the top-ten multiple times in hockey and baseball contests with thousands of competing entries.” Then they donated the money — “around $15,000” — to the Greater Boston Food Bank, which I thought was a classy touch.
A good chunk of the paper is theorems and proofs about submodular functions. I skipped those. What I wanted was the strategy: what they stacked, what they found, and whether any of it should change my tool. One thing did, and it made the tool more than twice as fast.
