a Google Glass based sports training product that gives sportsmen actionable information about improving their game during practice. The product makes elite- level performance training accessible to anyone without having the need to access an expert coach

1. democratizing elite-level sports performance
SPORTSWARE

2. my .net idea is...
a Google Glass based sports training product that gives
sportsmen actionable information about improving
their game during practice. The product makes elite-
level performance training accessible to anyone
without having the need to access an expert coach
SPORTSWARE

3. The Problem
millions of aspiring sportsmen around the world don't
have access to elite-level sports performance training
because top-notch coaches are a scarce resource
SPORTSWARE

4. The Solution
SportsWare provides sportsmen real-time, actionable
advice about possible actions, strategies, and outcomes
during a sparring game.
For example, a golf player is given a potential range of
shots that maximize his pay-off. Insights are drawn from a
vast data set that is personalized to the player based on
his historical performance data, and where he is in the
learning curve.
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5. How it works
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6. Golf Market Size
60M
# people that
play golf
$1K
# average price
per year
Data source: http://www.ask.com/answers/68613421/how-many-people-play-golf-worldwide
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x = $60 B
Total Addressable
Market Size
300K
#people that
play pro golf
x = $3 B
Served Available
Market Size
10%
market that
we can reach
SAMx = $300M
Target Market Size
$1K
# average price
per year

7. Oh, and by the way...
424M
# people that
play sports professionally
Data source: http://wiki.answers.com/Q/How_many_people_play_sport_in_the_world
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the market that we spoke about was just for
golf. The earnings potential increases by an
order of magnitude if we include other sports!
$420B TAM
$42B SAM
$4.2B SOM

8. Feasibility
All sports are based on a finite set of rules such that
the possible outcomes at any given point are finite
Rule based problem sets lend themselves well to
Machine Learning (ML)
There exists robust open source technologies to
solve ML, Computer Vision, and Big Data problems. Ex.
MLOSS, Open CV, Mahout & R
Sports data can be purchased from associations
such as the PGA, WGA, SRI, etc.
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9. Can be done
Google Glass will be available in 1-2 years from now. It
has a fairly powerful SDK on which to build apps on
There are algorithms that can simulate near perfect
datasets based on the availability of finite training data
In short, building this product is very feasible!
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10. Potential competition
Companies that have experience with Computer Vision,
Big Data, Predictive Analytics, Machine Learning
– Google
– IBM
– SportVision
– SAP
– HawkEye
– QSTC
– Advanced Motion Measurement
– Oracle
– Synergy Sports
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11. Get in touch
SPORTSWARE
Siddharth Balaravi
E: sid.rt@hotmail.com