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Methodology

Baseball

Sports Info Solutions has been at the forefront of baseball data and analytics for over 15 years. The company has been a driving force behind the Sabermetric Revolution and the Moneyball Era, due in part to groundbreaking analytical contributions such as Defensive Runs Saved (DRS).

Beginning in 2016, John Dewan led a group of colleagues on a venture to build the most comprehensive predictive model imaginable using its extensive database and analytical prowess. From this project spawned the original “Model 1”, which has undergone extensive testing and development behind the scenes to eventually lead us to the premier DFS tool that is now available for the 2019 MLB season – “SIS Model 4”.

In the advent of legalized sports betting, Dewan and company have adapted some of the methods behind the model and tailored it towards sports betting to be used as a leading source of predictive information to help inform sharp prop betting. A series of factors are weighted and synthesized to come up with the sharpest predictions using the following elements:

First, elements involving the player themselves are analyzed and weighted, such as:

— Player age and how each stat is specifically affected by his age
— Career performance
— Recent performance and specific recency weightings for each stat
— Home park of the player (where they play half their games)

Secondly, elements that affect player performance based on game conditions are considered and evaluated:

— Performance vs. the handedness of the opposing pitcher
— Quality of the opposing pitcher including how well the pitcher performs based on batter handedness
— How the park affects performance (including specialized Coors Field analytics)
— Temperature at game time
— Wind speed and direction at game time
— Pitchers-friendly umps and hitter-friendly umps
— Where the hitter hits in the lineup
— Quality of other players in the lineup
— How offensive support affects a pitcher’s likelihood to get a win
— How each stat is independently affected by each one of these elements
— How individual player tendencies and relevant timing data affect stolen bases

The system also features:

— Speedy updates thanks to SIS Video Scouts and IT staff churning out the latest roster, lineup, and injury information.
— SIS’ proprietary Defensive-Independent Batting & Pitching Statistics (DIBS & DIPS) formula, which uses batted ball characteristics (velocity, trajectory, and location) to determine expected outcomes for every ball in play.
— The renowned player projections system derived by Bill James and John Dewan


Football

Sports Info Solutions is the premier source for football charting data. As an industry-leader in baseball data collection and analytics for nearly two decades, SIS began applying the same techniques and principles to the gridiron in 2015. Over that short time, the SIS football brand has established itself as a key resource to NFL front offices for its in-depth data collection and cutting-edge research.

In addition to the role they play in pro team circles, SIS also stands out as the main NFL data provider for several major fantasy sports sites.2019 marks the fifth season that SIS has produced football projections, analyzing and improving their model every step of the way. The SIS staff is made up of football experts who input knowledge from a variety of professional backgrounds including pro scouting, research, fantasy analysis and sports forecasting.

Projecting performance is all about opportunity, so the model starts by identifying how many plays each team projects to have and what the run/pass split will be based on recent trends by both the offensive and defensive team. Then the model takes information from SIS’s constantly-updated depth charts to project the workload of the skill players. The split will depend on both the quality of the players involved and the team’s recent tendency. For example, if a team’s top receiver is projected to miss the next game, the next man up will be projected for an uptick in targets corresponding to his depth chart position, but not to the same extent that the true top receiver would (and the rest of the targets will trickle down to other pass-catchers accordingly).

After the initial playing time projections are generated, each team’s breakdown is reviewed for inconsistencies with any information that the model wouldn’t know about, like a player being limited as he returns from an injury, and the projections are re-calculated with that information added.

From there, each player’s per-play performance is projected using a weighted average of his previous performance, weighting each game using the number of days since that game. This allows the model to take into account multiple years of data to set a stable baseline, but also allows performance in a new season to inform the projections more quickly than week-to-week changes within a season. Those per-play metrics are then adjusted based on the quality of the opponent in limiting attempts, yards, touchdowns, etc.

Advancing the state of football analytics is their primary objective and SIS confidently puts forth their 2019 projections as their recipe for a consistent edge.

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