Case Studies

A Case study – SAP’S HANA in-memory technology for sports analytics and predictive tools in Cricket

By S.Ramaneti –Lai Uk

Take an instance any roomful of fans of, say, cricket or football, and we can guarantee that there will be at least one person there with an encyclopaedic knowledge of the sports, That would anything from Highest scores to batting averages and strike rates. There’s something about sports that attracts. But here’s another sure aspect to it, knowing about past performance is about to become old hat. The smart in thing now is on using big data to predict future sporting outcomes and sports analytics promises to be the bookmaker’s worst ever nightmare.

Applying Sports analytics to Cricket I wonder what will happen if we can think of the following instances where if Cricket Team Selectors and Captains apply some of the Big Data concepts to cricket will that make a difference it is basic premise in cricket that it is a team that wins or loses but there is a emphasis on the ability of an Individual as opposed to measuring individuals in the capacity of how they help the team.

1)  If a cricket team gets two batsmen to replace Sachin Tendulkar and still collectively get 100 runs out of them or have two not-so-great bowlers to replace Shane Warne and still get the other side out? If an eventual goal is to score, say 300+ runs in an ODI match does it matter how the runs are scored? What if you could find four players scoring 50 runs each instead of counting on Sehwag and Tendulkar types to score a century and lose miserably when they don’t?

 2) Or when a fielder drops a catch could it be because the ball came too quickly to him, he was at the wrong position, or he was just too lame to catch it.

 3) Or Is there a difference between a batsmen getting caught near the boundary as opposed to getting bowled? Currently, none. But, if a batsman gets caught, at least that batsman put the “ball in play.” A little more practice and precision and that could have been a four or a six.

 4)  In cricket, Often right handed bowlers switch from over the wicket to round the wicket mostly when whatever they are trying is not working. These decisions are not necessarily based on any historic data. In this case, it could be as simple as gathering and analyzing data about which batsmen have poor performance when bowled round the wicket as opposed to over the wicket. But In contrast taking an example of baseball, using a left-handed pitcher against a left-handed hitter and using a right-handed pitcher against a right-handed hitter have proven to work well in most cases (with some exceptions). That’s why there are switch hitters in baseball to take this advantage away from a pitcher. Why are there no switch hitters in cricket?

 5) Why can’t there be a dedicated bowler to finish the last over of the cricket match just  Imagine a precision bowler — a bowler who is trained as a “closer” — whose job is to throw six deliveries, accurately at a spot, fast or slow.  The regular bowlers are trained to bowl up to 10 overs, 6-8 at once, with a variety of deliveries (pitches) and a mission to stop batsmen from scoring runs and getting them out. A closer would only have one goal: stop batsmen from scoring. Historically, there have been a very few good all-rounder’s in cricket. It’s incredibly difficult to be a great batsman as well as a great bowler, but there’s a middle ground – to be a great batsman and a closer. Some batsmen such as Sachin Tendulkar have been good at bowling off and on when the regular bowlers get in trouble, but invariably their task becomes getting a wicket to break the partnership. Even if wickets are important, in most cases, it’s the ability to stop the opposite team from scoring in the last couple of over’s that brings team a victory.

 6) Bowling and batting power plays are relatively a new concept in cricket. Skippers on either side don’t have access to deep analysis of current situation and performance of opposite players in deciding when to take a powerplay. They make such crucial decisions based on their gut feeling and opinion of key players on the field. I think this is where data can do wonders.

I do understand The game of cricket differs so much from a test match to one day international (ODI) to Twenty20. But, a fresh look at data and analysis on what really matters and courage to implement those changes could do wonders.

If you look at other sporting events like football or Formula one or  for that matter most recent Wimbeldon were in Ball tracking Technology was introduced that can detect the pace at which a serve was fired down or whether a shot ruled “out” grazed enough of the tramline to be called “in.” IBM predictive analytics tools were in 39 Million data points gleaned from seven years of Grand Slam tennis matches to determine players’ patterns of play — their propensity for using their forehand, their first serve percentage, their willingness to volley. The historical data is then compared with footage from three-dimensional cameras dotted around the courts that show how players are performing in a live match, enabling to identify the three critical aspects of play that will determine the winner of the match.

Even if , there is a world of difference between old-school data analysis problems that may have relied on a meticulously created data warehouse built on  technological advances such as multicore processors and commodity network-attached storage devices that have made it feasible to contemplate such data-intensive pastimes with today’s fast-moving sports analytics and predictive tools.

A prime example of how these technological leaps are affecting sports teams can be seen at Formula One racing team McLaren. The makers of race cars have long had a gimlet-eyed focus on squeezing every last drop of performance improvement possible out of car design. But increasingly, this is accompanied by a fanatical dedication to tracking what is going on out on the track in the white heat of a Grand Prix race.

McLaren’s cars send a torrent of data back to the pit teams; the information is analysed in real time using SAP’s HANA in-memory technology. HANA employs data compression technology, which enables McLaren to store the data in random-access memory, ensuring that it can be analysed in the blink of an eye — and as a result, the team can act on the incoming data in time to make race-changing adjustments.

But I do understand that Tennis and Formula One racing represent the first generation of what might be possible with real-time data analysis, in those sports, the power of the analytics is concentrated on individual entities — cars or players. “The problem becomes exponentially more difficult when we look at team sports, such as Cricket, where there are 11 players on each side, each of whom can have an impact on the outcome.” But again the key to being able to generate insight from all the data being collected is having people with domain expertise who can provide the context for that particular data.

“At the moment I understand, everyone has different views on what works in cricket,”

So for the short term at least it looks like the bookies might still have some sports where people will be willing to take a punt on the outcome.