Hacker by passion, Stanford MSEE grad, and a data scientist with an insatiable intellectual curiosity. He continuously is honing his machine learning and statistical modeling skills to extract actionable insights from a maze of complex data. This data-driven perspective was critical in deploying the first predictive health analytics platform in the automobile industry @ Tesla, forecasting advertising spend potential to find the next ecommerce giant @ Google, helping North America acquire its first major esports championship in well over a decade @ Cloud9, and continuing to push the status quo in esports analytics by spearheading the data science division @ Evil Geniuses.
- Soham’s background on how he got to where he is now (00:45)
- Film analysis, debriefing after matches and setting up practice (02:57)
- Communication between players, coaches, and analysts (06:14)
- Developing an understanding of how and what to learn and becoming pro(09:03)
- Strategies for setting up productive practice for the coaches and players(14:04)
- Using data to help accomplish in-game outcomes (27:14)
- 3 key points to focus on to level up your game (30:19)
And much, much more!
Check out the full video here.
Some key takeaways, among many others, from our discussion…
“Learning how to learn is just as important in esports as it is in life.”
This can also be one of the biggest obstacles for young players who are climbing the ranks as an aspiring pro. It can be a difficult path for players who have no guidance in esports, contrary to traditional sports with little league leading into high-school sports, then the collegiate level, then minor leagues to major leagues. Esports, with its current infrastructure, is still being built out and there is not much guidance for what the best practices are to reach the next level.
Creating an infrastructure along with methodologies for learning and teaching is a huge opportunity for teams and players to take these concepts and apply them not only to gaming, but to life in general. It becomes a balancing act of understanding your learning styles and being able to distinguish your strengths and weaknesses as you build up your foundational and specific skill sets.
Practice time is finite, so being able to optimize those teachable moments is crucial.
For professional teams, in any given week you could play anywhere from five or six practice games a day, five days a week. That’s thirty game repetitions of meaningful practice often against some of the best teams in the world. Generally, there’s usually two scrim (practice) blocks per practice day. There’s a three-hour block with one team, then there’s a break, and then there’s a three-hour block after. When thinking about practice, it’s important to distinguish, are you playing to win or are you playing to practice which sets up the practice context for that block of training. Make it count.
Three things to focus on to level up
- Be okay with failing, especially the young players. Grow and learn from it.
- There’s a place for pick up games, but don’t make that your primary way to practice. If you are trying to practice outside of a team environment, go into a server by yourself and visualize and do the repetitions that you would do in a team environment.
- Have a process for yourself and how to get better. So whether it’s a pickup game, whether it’s after a scrim, whether it’s after your match, whether it’s after a team talk, it doesn’t matter. If it’s after something that you’re investing your time into during the day, you should reflect, and that reflection process looks different for everyone.
To listen to the full interview check out the link here.