GameRank: Ranking and Analyzing Baseball Network

In the paper we present an algorithm called Game Rank, modified from Page rank and HITS, to evaluate the pitching and batting ability for players in Major League Baseball (MLB) with a network perspective. The model could also be easily expanded and applied on any network that has multiple factors interacting with each other, to quantify the vertex´s significance. Then, we evaluate the algorithm by comparing its results to ESPN Ratings, a popular baseball rating method. Our algorithm achieves similar or better results with a way simpler model. Furthermore, relevant analysis is also performed for our MLB data network, with a few interesting conclusions drawn, like (a) players are getting closer in their skills, (b) good pitchers bats better than normal ones. What´s more, we have wrapped up the whole system as a working website, called MLB Illustrator (http://mlbillustrator.com), to let users interact with the data and network itself, making the traditional baseball statistics analysis based on tables and simple graphs evolve into intuitive visualized network analysis. At last, we present a series of examples where Game Rank model can be used, to prove that our model is extensive and widely applicable. Our contribution lies in the following aspects: (a) we provide a simple model to rank the nodes in networks with multiple indicators interplaying with each other, which expands the functionality of Page Rank, and is widely applicable, (b) we initially apply the network theory on the baseball network, handle a set of analysis on it, and have some interesting findings, (c) we provide a powerful method to rank baseball players which is stronger than ESPN Ratings in several aspects.