Fantasy football, a game based on starting players from various teams to gain points based off of their respective performance, gives great delight to sport nerds. With data, players tend to favor players based on raw stats over favorability. In this instance, a player would prefer to pick a rival team’s quarterback over his favorite team’s quarterback if the rival puts up better stats. This simplistic data analysis for these games is only a start. Some hardcore people will use real time algorithms to create fully optimized teams that win a majority of the time. These people will go on gambling sites based on fantasy football to win thousands of dollars. Though some people handle data at a much greater magnitude than others, people who play this game need to use some data to stand a chance. Beyond people playing fantasy football, the actual NFL and other professional leagues use Statistics and data to greater lengths than ever before. This could range from organized gambling to player selection. Maximizing gambling does have its consequences if fights are rigged. For example, Statistical analysis of Japanese sumo wrestling discovered fixed fights (Data, data everywhere). That sumo wrestling league tried to maximize betting gains by rigging fights; doing so created statistically unlikely occurrences of fight results. In that situation, Statistics provided a means of stopping a corrupt league and its gambling circuit. After the game itself, setting good prices for tickets, establishing cable contracts, and advertising require using Statistics to optimize financial gain. As with marketing in general, a company, in this case the NFL, must determine the relation between economic gain from advertising and the cost of advertising to provide the most economic benefit. Furthermore, analyzing supply and demand of tickets allows the NLF to price
Fantasy football, a game based on starting players from various teams to gain points based off of their respective performance, gives great delight to sport nerds. With data, players tend to favor players based on raw stats over favorability. In this instance, a player would prefer to pick a rival team’s quarterback over his favorite team’s quarterback if the rival puts up better stats. This simplistic data analysis for these games is only a start. Some hardcore people will use real time algorithms to create fully optimized teams that win a majority of the time. These people will go on gambling sites based on fantasy football to win thousands of dollars. Though some people handle data at a much greater magnitude than others, people who play this game need to use some data to stand a chance. Beyond people playing fantasy football, the actual NFL and other professional leagues use Statistics and data to greater lengths than ever before. This could range from organized gambling to player selection. Maximizing gambling does have its consequences if fights are rigged. For example, Statistical analysis of Japanese sumo wrestling discovered fixed fights (Data, data everywhere). That sumo wrestling league tried to maximize betting gains by rigging fights; doing so created statistically unlikely occurrences of fight results. In that situation, Statistics provided a means of stopping a corrupt league and its gambling circuit. After the game itself, setting good prices for tickets, establishing cable contracts, and advertising require using Statistics to optimize financial gain. As with marketing in general, a company, in this case the NFL, must determine the relation between economic gain from advertising and the cost of advertising to provide the most economic benefit. Furthermore, analyzing supply and demand of tickets allows the NLF to price