User:Owenlynch8/sandbox

Gone are the days where coaches and staff rely on gut feelings to make their strategic decisions. Current basic descriptive models that are used to measure advanced statistics are Corse, Fenwick, PDO, and more complicated models such as xGF (expected goals for), and relative models such as CorsiRel, use linear and multiple regression techniques in order to chart scoring chance and possession metrics relative to both team-mates and the entire league in short, medium, and long term sample sizes. Four metrics of the Fenwick metric are Fenwick for at even strength shots + misses (FF), Fenwick against at even strength shots + misses (FA), Fenwick for percentage at even strength (FF%), and relative Fenwick for percentage (FF% rel). Thomas Chabot of the Ottawa Senators leads the entire NHL in both FF and FA. Fenwick is basically the same idea as Corsi, however, it does not count the blocked shots in the stat. Only including shots on goal and shots wide gives credit the idea that blocked shots are intentional and could be a part of a coach’s system. Many of the ideas of Corsi apply to Fenwick. Fenwick for percentage (FF%), Fenwick plus-minus (F± or F+/-).

Average performance of PGA TOUR players was used as the benchmark to compare performance, and strokes gained then used to explain the contributions of each shot to the total score. Several studies have looked at sequential variance of consecutive golf scores across both holes and rounds. 6–8 Round scores showed relatively weak correlations to scores on consecutive rounds, when external influences on performance were considered. Scores between successive holes also showed weak correlations when external influences like par and difficulty were considered (weather conditions, course setup, etc.). Other than the obvious fact that good players tend to shoot low scores, and poor players tend to shoot high scores, the results suggested performance in golf is not subject to ‘streakiness’ and performance on individual shots. Some current metrics used by the PGA are driving percentage accuracy, greens in regulation percentage, and scrambling (miss green in regulation but still make par or better)

The NFL benefits from being a stop and start game, so a new set of data can essentially be taken from every snap. There are advanced metrics used for offense, defense, and special teams. Some advanced metrics used in the NFL regarding passing are intended air yards (IAY), completed air yards (CAY), and pass yards after catch (YAC). The Tampa Bay Buccaneers lead the NFL in both IAY and CAY. Advanced passing can be broken down into air yards, accuracy, pressure, and play type to analyze success (or failure). Regarding running, one metric currently being used is expected rush yards per play using the performance of the individual ball carrier, contributions from the offensive line, scheme and situation. Route recognition takes conventional stats such as receptions and receiving yards and advanced stats like depth of target, separation window, and completion probability to determine which routes are most effective.

Much like the NFL and the MLB, the NBA benefits from having a stop and start style of play. There is essentially a 'reset' after each basket, foul, or out of bounds, which allows the following play to be analyzed from a clean slate. The use of data analytics in the NBA allows teams to design winning strategies, predict and avoid player injury, and more efficiently scout up and coming talent. Entire teams can be measured, or it can be on a player by player case, from metrics that range from fantasy points scored for fan duel to strength of schedule for a team. . Use of data analytics has definitely changed NBA games, but until data models are perfect, analysts should consider other factors when making decisions especially those that involve human psychology

Professional Golf Association (PGA) Tour[edit]
The PGA Tour collects vast amounts of data throughout the season. These statistics track each shot a player takes in tournament play, collecting information on how far the ball travels and exactly where each shot is played from and where it finishes. These data have been used for a number of years by players and their coaches during practice sessions as well as during tournament preparation, highlighting the areas in which that player needs to improve before teeing it up in tournament play. Average performance of PGA TOUR players was used as the benchmark to compare performance, and strokes gained then used to explain the contributions of each shot to the total score. Several studies have looked at sequential variance of consecutive golf scores across both holes and rounds. 6–8 Round scores showed relatively weak correlations to scores on consecutive rounds, when external influences on performance were considered. Scores between successive holes also showed weak correlations when external influences like par and difficulty were considered (weather conditions, course setup, etc.). Other than the obvious fact that good players tend to shoot low scores, and poor players tend to shoot high scores, the results suggested performance in golf is not subject to ‘streakiness’ and performance on individual shots. Some current metrics used by the PGA are driving percentage accuracy, greens in regulation percentage, and scrambling (miss green in regulation but still make par or better)

Shotlink data collection has revolutionized the way that data is collected in the game of golf. Introduced on a full-time basis in 2003, Shotlink relies on a number of strategically placed on-course laser rangefinders and cameras to collect precise data from every shot that is struck on the PGA Tour. With these data, players are able to see the areas of their game that need improving, and on a broader year-to-year basis, players can review course statistics from previous years to allow for relevant tournament preparation. On top of the year-to-year stats provided players and fans can also easily access these statistics at an up to the minute rate, giving these data an extremely high velocity. Shotlink has also made its mark on the world of golf course design as designers have constant access to up to the minute statistics of professional golfers, allowing for these designers to create courses that can provide a challenge for the world's best players


 * Driving Accuracy Percentage is a measurement of how frequently the golfer's balls end up on the fairway after their tee shot on each hole that they play. While the correlation isn't entirely direct, the higher that a golfer's driving accuracy percentage is, the higher they place overall in the tournament because it is significantly easier to hit your second shot from the middle of the fairway than deep in the rough. While the elite outliers will consistently place at the top regardless of their DAP, this statistic can be helpful to differentiate the middle of the pack.
 * Green in Regulation Percentage is a measure of how frequently a golfer is able to reach the green on a hole within the allotted regulation amount of strokes. A ball is considered to be on the green in regulation if any portion of the ball is touching the green after the GIR stroke has been taken. The GIR stroke is taken by subtracting 2 from the par of the hole. Ex: 3 on par 5, 2 on par 4, 1 on par 3.
 * Scoring Average is the weighted scoring average which takes the stroke average of the field into account. It is computed by adding a players total strokes to an adjustment and dividing by the total rounds played. The adjustment is computed by determining the stroke average of the field for each round played. The average is subtracted from the par to create an adjustment for each round. A player accumulates these adjustments for each round played.

The NFL benefits from being a stop and start game, so a new set of data can essentially be taken from every snap. There are advanced metrics used for offense, defense, and special teams. Given the combination of being a team game with stop and start play, the NFL is a sport that has vast amounts of analytics that can be interpreted to give teams a competitive advantage in a given scenario. It is extremely beneficial when approaching a play to have an idea of what your opponent is going to do and what you can do to give yourself the greatest chance of success. While there are countless variations of analytics that can be broken down, here are some of examples of the main ones used:


 * Expected Rushing Yards uses the performance of the individual ball carrier, contributions from the offensive line, scheme and situation to help quantify how many yards a team can expect per rush on a given play. For example, Nick Chubb ran for an 88 yard touch down last year, and there was a less than 1% chance of that occurring. However, there was a 12.4% chance that he'd gain a first down and a 52.1% chance that he would gain 6+ yards
 * Expected Yards after Catch uses the same model as expected rushing yards. The EYAC model can also predict occurrences such as first downs and touchdowns
 * Route Recognition uses receptions, receiving yards, depth of target, separation window, and completion probability to determine which routes yield the greatest likelihood of success. This provides greater insight, and leaves the question, which route did the pass catcher run to get open before catching the ball.
 * Live Win Probability predicts the chance of a team winning at any moment between plays in the game. This looks at the score differential, down-and-distance, time remaining, timeouts remaining, expected points and team quality, all based off data that spans over the last decade. This is simply a probability, because as seen throughout the 2020 NFL seasons, many teams can have nearly a 100% chance of winning deep in the 4th quarter and still lose.

Specifically regarding some metrics that go into sports betting data analytics algorithms:


 * Team/Player Stats: Use stats such as points for and points against and weigh them to help determine spreads and money lines
 * Team/Player Performance: Tracks how well teams have performed against the spread and odds that have been set against them
 * Betting data: The cumulative data that is put together that contains all information of how bettors bet on a given night, typically against a team, or odds that people are most likely to bet on.

Matchup grades, line analysis, and odds tracking are all important factors when using analytics to place a bet. However, there are many different sub-categories that go into these decisions:


 * Best Bets: The optimal bets that can be placed that will yield the highest profit given a reasonable amount of risk.
 * Predicted Performance: Estimate of the outcome of what is about to take place given the matchup
 * Line Analysis: Used to identify lines that would be considered non-favorable for bettors to take, whether it be because there is too much risk or not enough payout
 * Public Money: Tracks what the general population is trending towards in a given contest
 * Odds Tracking: Not all lines are the exact same on each book, even if it is the same game. It is important for a bettor to find the odds that are most favorable for them so they can turn the largest profit should the bet hit
 * Futures Trends: Seeing how teams and sports trend betting wise over the long term as opposed on a nightly basis. Ex: If a certain team, regardless of their record has covered the spread in the last X amount of contests.
 * Bet Optimizers: Used to determined bets where it can be worth it to place a parlay or a teaser as opposed to a straight bet
 * Pro Bettor Report: A report that shows where "professional" gambler are placing their money
 * Daily Fantasy Sports Tools: Not all sports bets are on sports games. Many books, such as draft kings use fantasy line ups. This tracks who is best to play or sit on a given night to help your chances of winning.