1. What new analytics are fueling baseball decisions?
[I spoke at Nerd Nite SF about recent developments in baseball analytics. -Tracy Allison Altman, Ed.] Highlights from my talk:
– Data science and baseball analytics are following similar trajectories. There’s more and more data, but people struggle to find predictive value. Oftentimes, executives are less familiar with technical details, so analysts must communicate findings so they’re palatable to decision makers. The role of analysts, and challenges they face, are described beautifully by Adam Guttridge and David Ogren of NEIFI.
– ‘Inside baseball’ encounters outsiders with fresh ideas. Bill James and Billy Beane are the obvious/glorious examples. Analytics experts joining front offices today are also outsiders, but valued because they understand prediction; the same goes for anyone seeking to transform a corporate culture to evidence-based decision making.
– Defensive shifts will exceed 30,000 this season, up from 2,300 six years ago (John Dewan prediction). On-the-spot decisions are powered by popup iPad spray charts with shift recommendations for each opposing batter. And defensive stats are finally becoming a reality.
– Statcast creates fantastic descriptive stats for TV viewers; potential value for team management is TBD. Fielder fly-ball stats are new to baseball and sort of irresistible, especially the ‘route efficiency’ calculation.
– Graph databases, relatively new to the field, lend themselves well to analyzing relationships – and supplement what’s available from a conventional row/column database. Learn more at FanGraphs.com. And topological maps (Ayasdi and Baseball Prospectus) are a powerful way to understand player similarity. Highly dimensional data are grouped into nodes, which are connected when they share a common data point – this produces a topo map grouping players with high similarity.
2. Will AI replace insurance actuaries?
10+ years ago, a friend of Ugly Research joined a startup offering technology to assist actuaries making insurance policy decisions. It didn’t go all that well – those were early days, and it was difficult for people to trust a black box model. Skip ahead to today, when #fintech competes in a world ready to accept AI solutions, whether they augment or replace highly paid human beings. In Could #InsurTech AI machines replace Insurance Actuaries?, the excellent @DailyFintech blog handicaps several tech startups leading this effort, including Atidot, Quantemplate, Analyze Re, FitSense, and Wunelli.
3. The blind leading the blind in risk communication.
On the BMJ blog, Glyn Elwyn contemplates the difficulty of shared health decision-making, given people’s inadequacy at understanding and communicating risk. Thanks to BMJ_ClinicalEvidence (@BMJ_CE).