On the existence of momentum in professional football.

Using United States National Football League play-by-play data for the 2002-2012 seasons, we train a neural network to predict win probability, based on measures of the game state. This predictor's performance is comparable to the point spread at the start of the game and improves thereafter with little bias. We define a measure of success as the change in a team's win probability over the course of a possession, and show that streaks in this measure are highly unlikely to be random.

Does the principle of investment diversification apply to the starting pitching staffs of major league baseball teams?

Financial advisors often emphasize asset diversification as a means of limiting losses from investments that perform unexpectedly poorly over a particular time period. One might expect that this perceived wisdom could apply in another high stakes arena-professional baseball-where player salaries comprise a substantial portion of a team's operational costs, year-to-year player performance is highly variable, and injuries can occur at any time. These attributes are particularly true in the case of the starting pitching staffs of professional baseball teams.

NBA team home advantage: Identifying key factors using an artificial neural network.

What determines a team's home advantage, and why does it change with time? Is it something about the rowdiness of the hometown crowd? Is it something about the location of the team? Or is it something about the team itself, the quality of the team or the styles it may or may not play? To answer these questions, season performance statistics were downloaded for all NBA teams across 32 seasons (83-84 to 17-18). Data were also obtained for other potential influences identified in the literature including: stadium attendance, altitude, and team market size. Using an artificial neural network, a team's home advantage was diagnosed using team performance statistics only.

Peer review, program officers and science funding.

Increased competition for research funding has led to growth in proposal submissions and lower funding-success rates. An agent-based model of the funding cycle, accounting for variations in program officer and reviewer behaviors, for a range of funding rates, is used to assess the efficiency of different proposal-submission strategies. Program officers who use more reviewers and require consensus can improve the chances of scientists submitting fewer proposals.