Swish Analytics is a technology company.
Swish Analytics has raised $7.0M across 1 funding round.
Swish Analytics has raised $7.0M in total across 1 funding round.
Swish Analytics has raised $7.0M in total across 1 funding round.
Swish Analytics's investors include AME Cloud Ventures, Chemistry VC, FTX Ventures, Hardware Club, Penny Jar Capital, Simon Equity Partners LLC, Telstra Ventures, Colin Carrier.
Swish Analytics is a San Francisco-based technology company specializing in predictive sports analytics, betting, and fantasy sports data products. Founded in 2014, it builds advanced algorithm-driven systems that project outcomes for every statistic in major U.S. sports leagues such as the NFL, NBA, MLB, and NHL. Their core product suite includes odds origination, risk management, and trading software designed for sports betting operators and fantasy sports players. Swish Analytics serves sports bettors, fantasy players, and operators by providing data-driven, forward-looking insights that replace intuition with engineering, mathematics, and machine learning. This helps users make smarter bets and lineups, effectively reducing research time and improving decision-making accuracy[1][2][4][5].
Swish Analytics was co-founded by Bobby Skoff and other founders with backgrounds in data science and sports analytics. The idea emerged from the recognition that traditional sports data was largely backward-looking, focusing on past performance rather than predictive insights. The founders saw an opportunity to leverage big data, machine learning, and algorithmic modeling to create a real-time, predictive analytics platform for sports betting and fantasy sports. Early traction came from developing proprietary metrics and machine learning algorithms that identified inefficiencies in betting lines set by Vegas, enabling users to capitalize on these inaccuracies. The company initially raised angel funding and has steadily grown its user base and product offerings since its 2014 inception[1][4][5].
Swish Analytics rides the wave of big data and machine learning transforming sports betting and fantasy sports. The timing is critical as the U.S. sports betting market has exploded following legalization in many states, with nearly $400 billion wagered annually in the U.S. alone. Traditional sports analytics have been backward-looking, but Swish’s forward-looking predictive models meet the growing demand for real-time, actionable insights. This trend is fueled by advances in data science, increased availability of granular sports data, and the rise of daily fantasy sports and in-play betting. Swish’s technology not only empowers bettors and fantasy players but also influences media companies, sports teams, and game makers by providing predictive data that can enhance fan engagement and content creation[4][5].
Looking ahead, Swish Analytics is positioned to expand aggressively across different sports and betting markets, leveraging its algorithmic expertise and scalable technology platform. As sports betting continues to grow and diversify, trends such as in-play betting, player proposition markets, and integration with media and gaming platforms will shape Swish’s evolution. Their influence may extend beyond bettors to become a key data provider for sports media, teams, and gaming companies seeking to enhance fan experiences with predictive insights. Continued innovation in machine learning and real-time data processing will be critical for maintaining their competitive edge and expanding their user base[5].
Swish Analytics exemplifies the shift from intuition-based sports betting to data-driven decision-making, setting a new standard for predictive sports analytics in a rapidly evolving market.
Swish Analytics has raised $7.0M across 1 funding round. Most recently, it raised $7.0M Series B in May 2019.
| Date | Round | Lead Investors | Other Investors |
|---|---|---|---|
| May 1, 2019 | $7.0M Series B | AME Cloud Ventures, Chemistry VC, FTX Ventures, Hardware Club, Penny Jar Capital, Simon Equity Partners LLC, Telstra Ventures, Colin Carrier |