Peer Review, Program Officers and Science Funding

Peer reviewe

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. Accordingly, this study analyzes starting pitcher performance and financial data from all Major League Baseball teams for the period 1985-2016 to determine whether the standard investment advice is applicable in this context, understanding that the time horizon for success for an investor and a baseball team may be distinct. A multiple logistic regression model of playoff qualification probability, based on realized pitcher performance, measures of luck, and starting pitcher staff salary diversification is used to address this question. A further stratification is conducted to determine whether there are differences in strategy for teams with allocated financial resources that are above or below league average. We find that teams with above average resources increase their post-season qualification probability by focusing their salary funds on a relative few starting pitchers rather than diversifying that investment across the staff. Second, we find that pitcher performance must align with that investment in order for the team to have a high qualification probability. Third, the influence of luck is not negligible, but those teams that allocate more overall funds to their pitching are more resilient to bad luck. Thus, poorly resourced teams, who are generally unable to bid for pitchers at the highest salary levels, must adopt alternative strategies to maintain their competitiveness

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. Attendance, altitude, and market size were unsuccessful at improving this diagnosis. The style of play is a key factor in the home advantage. Teams that make more two point and free-throw shots see larger advantages at home. Given the rise in three-point shooting in recent years, this finding partially explains the gradual decline in home advantage observed across the league over time

Group 2 share (%) of available funding based on the reviewers.

<p>Shown as a function of the number of reviewers and the reviewers who are of the correct type (%). Selfish reviewers are fixed at 20% and the remainder are harried reviewers. The program officer is correct and initial funding decisions are made using (a) unanimous reviewer recommendations or (b) allowing one negative recommendation.</p

Proposal peer-review process.

<p>Proposal review process, accomplished by <i>K</i> scientists, randomly selected from the set of <i>N-1</i> scientists (excluding the scientist who submitted the proposal under consideration). <i>Qs (R)</i> is the scientific quality of the reviewer. Arrows indicate the flow of decisions through the proposal review process.</p

Program officer decision-making process.

<p>Proposal funding decision process, accomplished by the funding agency program officer. Arrows indicate the flow of decisions through the funding decision process.</p

Proposal generation and submission process.

<p>Proposal generation by <i>N</i> scientists, of quality <i>Qs</i>, drawn from a normal population of mean 100 and standard deviation 10. Proposals are of quality <i>Qp</i>, drawn from a normal population of mean <i>Qs</i> and standard deviation 5. Arrows indicate the flow of decisions through the proposal submission process.</p

Agent-based model experimental results.

<p>Results are stratified according to the two scientist groups. Shown for each group are the scientist funding success and the average quality of funded proposals. The group 2 share of the available funds is also shown.</p

Group 2 share (%) of available funding based on target funding rate.

<p>Shown as a function of the target funding rate (%) and the reviewers who are of the correct type (%). Selfish reviewers are fixed at 20% and the remainder are harried reviewers. The program officer is correct and initial funding decisions are made using (a) five unanimous reviewer recommendations or (b) allowing one negative recommendation.</p