'Beef'ing up Live cricket coverage
Live Match Analysis alongwith COW for all ODIs & T20Is at
www.holdingwilley.com
COW i.e. Chance of Winning is a tool built to predict, at the end of each over,
the chances each team has of actually going on to win the match. More on that here.
It takes into account all the variables - strength of the teams playing, resources
lost, resources left, pitch conditions and well, everything else, distills it down
to a single percentage and summarizes the past, present and future of the match
in question.
The tool is live for all international ODIs & T20Is and we would love to have your
thoughts on its results.
So if you find something wrong, something interesting, if you like it, or if you
hate it, if you feel it could be better, if you feel there is something you like
which is missing, drop us a mail at editor@holdingwilley.com.
Who let the COW out?
COW (Chance of Winning)
COW (Chance of Winning) is a program that predicts the outcome of an ODI or T20
cricket match from any given situation.
Initial thoughts
When I started writing for HoldingWilley, I was new to COW and could not understand
how it helped me understand an ongoing match better. It was fascinating at first,
to see all the factors from ground conditions to auto-adjusting weightages; but,
at the end of the day, they were just a bunch of numbers which kept changing with
changes in the score. I didn’t get many insights before the match started. A predictor,
according to me, should have told me the results well in advance, whereas COW gave
me 100% for a team only at the end of a match and 90% only when nearly all was lost
for the other team.
We know that a team is going to lose if they need to hit 50 runs off 10 balls. Why
do we need COW?
Then, on the other hand, I liked the concept of having a percentage tell me how
the game was proceeding. For example, an Irfan Pathan boundary halfway through an
innings never told me anything before I started following COW. With this, I knew
it increased India’s chances by 1% (why do we need to know that? read on, my friend…read
on).
On more gruelling and otherwise dry situations in the match (read ‘scoring by singles’
when 200 runs are needed off 180 balls), I didn’t know if the team had to go for
runs risking their wickets or play safe like they did and accelerate at the end.
COW told me exactly where their chances stood, how much it went down (or up) by
playing that way. There were other even more interesting things that COW was able
to tell me, but I’ll come to them in a while, after talking a little more about
these ‘Level-1’ insights.
The COW ‘Level-1’ Insights
Following COW is like following the score, but at another level. COW does a lot
of math I don’t even want to think about, leave alone sit and calculate, and tells
me which team has the upper hand at the moment. When we have two partnerships, say,
A being 40 (20) and B being 75 (60), I would usually think that both were crucial
and leave it at that. Now, I’ll know which one of them increased their chances more;
A by 12% and B by 10%.
Another interesting thing you can derive from COW is the effectiveness of the bowlers.
The figures of two bowlers could read the same “4-0-40-1” but that doesn’t tell
us one bit about the bowling performance. No, it doesn’t. I can hear you asking
‘how’ already, so I am directly jumping to a scenario so that you understand it
better.
Take two bowlers Sree and Zee from a twenty-twenty match, and let’s say they had
the same 40 runs in 4 overs figures against, say Australia. Consider the following
scenario.
Sree bowled overs 14, 16, 18 and 20 when the average required run rate was 12 (that
would be the average of the required run rates for the four overs he bowled). He
took the wicket of a tail ender.
Zee bowled overs 2, 4, 6 and 19 when the average required run rate was 7. He took
the wicket of the most dangerous batsman in the opposition for a duck.
While one guy maintained his economy rate below the required run rate, the other
guy stopped a batsman from decimating the target set by the bowling team. Can’t
say who bowled better (or worse)? COW can. It’s incredibly simple for the user to
calculate the overall effectiveness of the bowler just by seeing the total increase
/ decrease in the team’s chances during his overs. We can find the most impactful
moments in a match using COW in a similar way.
Who is winning? – How to predict the outcome of a current match using COW
The one final question in everyone’s mind, isn’t it?
I would now refer to two phrases that we so often hear in the commentary every day,
“Confidence of a team” and “Pressure on a team”. Statisticians have, for long, been
unable to predict the outcomes of matches to an agreeable percentage because they
only took the career potential and recent results of a team and not the huge factors
the aforementioned terms have been. COW, if you look more closely, quantifies the
current mood in the match to a certain extent.
Let us assume that we have a T20 team “AA” playing in a specific tournament against
a team “BB”. By looking at the COW records of the teams from the previous matches
in the tourney, we can find out a few things about the teams quantitatively.
At any position in the match, Team AA could have, say, a 44% chance of winning the
match (and hence 56% for Team BB). This means that, with the team’s potential and
the available resources, BB have a better chance of winning the match. BB’s players
would be confident of taking the match away and AA’s players would feel the pressure
breathing down their necks…the quantity being 6% of their chances.
Interesting to measure such complicated terms in terms of chances? Yes? There are
several other ways in which you can look at the COW numbers too, but that will require
a comprehensive guide rather than an article. So, I’ll get back to the original
point.
While this by itself could give us a few Level-1 insights, we have another level
of insights (Level-2) which helps us predict the outcomes. This would be checking
the current numbers with the data available from the team’s previous matches (or
history, to put it in another way). If Team AA have gone on to lose every match
if their chances went below 45% (usually much lower) without any recovery beyond
that, then the 45% mark would be their pressure-point. Oppositions should work on
taking it beyond that as quickly as possible and leave the crumble-under-pressure
job to AA.
Similarly, we can find out that Team BB has always won a match once their chances
have crossed, urm… 55%. Comparing this with the current data, we can ‘predict’ that
team AA would lose the match. Though the COW percentage is 44% (pretty much near
50%) and the team would have an achievable target at that point of time, team AA
would go on to lose the match at least 90 times out of 100. This percentage can
change during a tournament and would become steady and highly reliable near the
halfway stage of long tournaments like the IPL. However, each tournament, if with
a majorly different team composition, is a different entity and the same numbers
should not be used across them.
Of course, COW isn’t 100% accurate yet, nor can any program ever be...but I can
say this from the numbers I see every day; it is almost there.
So if you find something wrong, something interesting, if you like it, or if you
hate it, if you feel it could be better, if you feel there is something you like
which is missing, drop us a mail at editor@holdingwilley.com.