Personal blog on early retirement, a little traveling, and analytics projects. I write about what I learn as I am learning it.
Operations research in practice — vehicle routing, staff and shift scheduling, project scheduling and resource leveling, and the free optimization tools I built out of twenty years of Excel models. How the math actually works, and where it breaks.
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.
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.
My free lineup optimizers for MLB, NFL and NHL now pull two things you used to have to look up in another tab: the league’s injury report and the Vegas lines. The part I care about most is how the Vegas lines get used, because the first version of that rule quietly did the opposite of what I built it for. Implied team totals are the most useful number on a DFS slate that isn’t in the salary file, so this is the post about how the tool gets them, what it does with them, and the one rule I had to throw away.
The MLB lineup optimizer just got an upgrade that matters more than any of the lineup rules it already enforced: it now knows who’s actually playing. Confirmed batting orders and probable starting pitchers load automatically from Major League Baseball’s own schedule data, and two new lineup filters use that information to keep bench bats and non-starting arms out of your build by default.
Until now, the tool would happily stack a hitter who wasn’t in that day’s starting nine, or a pitcher who was penciled in for Thursday, not today. That’s on the player to catch by cross-checking DraftKings against a lineup site. Not anymore — the optimizer pulls the day’s confirmed lineups itself and marks every player accordingly, the same way the NHL version of this tool already does with starting goalies.
The NBA optimizer is taking shape, and the shape is strange: the stacking panel is empty, the position rules are the site’s own, and the one feature I actually care about is a number nobody else gives away for free. This is where NBA DFS variance comes in, and why it stopped being something you have to pay for.
Short version of the last three posts: basketball creates no linked scoring unit, so there is nothing to stack; I measured that across three seasons and the correlations are about an order of magnitude weaker than football’s; and the old Excel model I dug up had already reached that conclusion by deleting the columns. What survived all of that is one idea, and it came out of the spreadsheet.
