Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball
Authors: Andrew Miller, Luke Bornn, Ryan Adams, Kirk Goldsberry
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our data consist of made and missed field goal attempt locations from roughly half of the games in the 2012-2013 NBA regular season. These data were collected by optical sensors as part of a program to introduce spatio-temporal information to basketball analytics. We compare the fit of the low rank NMF reconstructions and the original LGCPs on held out test data in Figure 5. |
| Researcher Affiliation | Academia | Andrew Miller ACM@SEAS.HARVARD.EDU School of Engineering and Applied Sciences, Harvard University, Cambridge, USA Luke Bornn BORNN@STAT.HARVARD.EDU Department of Statistics, Harvard University, Cambridge, USA Ryan Adams RPA@SEAS.HARVARD.EDU School of Engineering and Applied Sciences, Harvard University, Cambridge, USA Kirk Goldsberry KGOLDSBERRY@FAS.HARVARD.EDU Center for Geographic Analysis, Harvard University, Cambridge, USA |
| Pseudocode | No | Given point process realizations for each of N players, x1, . . . , x N, our procedure is 1. Construct the count matrix Xn,v = # shots by player n in tile v on a discretized court. 2. Fit an intensity surface λn = (λn,1, . . . , λn,V )T for each player n over the discretized court (LGCP). 3. Construct the data matrix Λ = ( λ1, . . . , λN)T , where λn has been normalized s.t. P λn = 1 4. Find B, W for some K such that WB Λ, constraining all matrices to be non-negative (NMF). |
| Open Source Code | No | To compare various NMF optimization procedures, the authors used the r package NMF (Gaujoux & Seoighe, 2010). |
| Open Datasets | No | Our data consist of made and missed field goal attempt locations from roughly half of the games in the 2012-2013 NBA regular season. These data were collected by optical sensors as part of a program to introduce spatio-temporal information to basketball analytics. |
| Dataset Splits | No | We compare the fit of the low rank NMF reconstructions and the original LGCPs on held out test data in Figure 5. For each fold, we held out 10% of each player s shots, fit independent LGCPs and ran NMF (using the KL-based loss function) for varying K. |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory amounts) used for running experiments are mentioned in the paper. |
| Software Dependencies | Yes | To compare various NMF optimization procedures, the authors used the r package NMF (Gaujoux & Seoighe, 2010). |
| Experiment Setup | No | Though we have described a continuous model for conceptual simplicity, we discretize the court into V one-squarefoot tiles to gain computational tractability in fitting the LGCP surfaces. To overcome the high correlation induced by the court s spatial structure, we employ elliptical slice sampling (Murray et al., 2010) to approximate the posterior of λn for each player, and subsequently store the posterior mean. |