Momentum: How Important Is It Before A World Cup?
By: Amol Desai
Tags: Sports, Data Science, Cricket, Analytics, Cricket World Cup

Fig. 1 Wins & Losses in ODIs

Recently, we have seen Australia pick up some steam after losing to India at home, and beat India in India in ODIs. Similarly, we saw England outclass Pakistan after a small hiccup in the West Indies. On the other hand, the top three (arguably) from Asia have seen more losses than wins. New Zealand haven’t even played any ODIs in the last 3 months. Their last outing was against India in Feb.

Is this a reflection of how teams are prioritizing things? Are some teams looking to get into winning habits, while others are trying out new combinations and tactics? Are some teams exposing themselves to adverse situations without focusing on the result to make sure they are battle ready when it matters? And more importantly, does this winning momentum matter when it comes to success at the World Cup?

We will look at team combinations in a future post. Here, let’s look at whether momentum matters.

There is Momentum & then there is Momentum

You can have two types of momentum: sustained & emerging. Teams may dominate for long periods of time such as the Aussies of the early & mid 90s or produce inspired (& inspiring) performances like Pakistan at WC 1992.

I divided the time between successive World Cups since 1975 into 4 parts:

Fig. 2

Teams may have done well through all of these phases or have drops or surges in performance during periods approaching a World Cup.

What we cannot Observe, we have to Infer

We don’t have all teams playing each other in ODIs in each of the above periods for each World Cup. Some teams also played very few matches in a given period. So, we need a way to infer the relative success of teams during each of the periods.

Simply using win percent or count as illustrated in Fig.1 doesn’t capture the whole story. In a World Cup, the number of different oppositions you can win against matters. How good you are matters only relative to how good other teams are. A victory over a team that has beaten multiple other teams should have more weight than a victory against a team that everybody else also beat or against a team that played no-one else. Taking the last 3 months before this year’s World Cup as an example again, we see, that although Afghanistan has 5 wins at 62.5% against India’s 2 at 40%, both of India’s wins came against Australia, who also beat Pakistan 5–0, while Afghanistan’s wins were against Scotland & Ireland. Scotland didn’t play anyone else, while Ireland’s only victory from the 3 opponents it played came against Afghanistan.

Fig 3. Graph of Wins & Losses — Period B: Last 3 months before WC 2019

In order to capture these kinds of relationships between victories and defeats, I used the PageRank algorithm on a directed graph where edges go from the losing team to the winning team & the edge weights represent the number of victories in that direction.

One can see from Fig. 3 that South Africa, despite whitewashing Sri Lanka, has a low PageRank since the two teams only played against each other and hence there is weak evidence of dominance, while Australia & India having beaten each other and also being connected to the rest of the graph through Australia’s wins over Pakistan, lead the pack. England, despite a lot of wins in a well connected part of the graph, have a lower PageRank because they won against Pakistan who did not win at all, and were beaten by West Indies, who were beaten by Bangladesh, who didn’t lose a single match & won against one other team (Ireland). We will refer to PageRank as the Dominance Score from here on to keep things intuitive.

I chose not to use the Elo rating method for this study since I wanted to consider performance during the entire period under consideration and disregard the order of matches within a period. For example, using Elo, Bangladesh’s rank would have changed depending on the chronological order of their victories and defeats against the West Indies and West Indies’ victory over England. Elo & Trueskill are also Bayesian in nature, and I explicitly wanted to avoid imposing prior assumptions on team performance in each period.

Pushing for Dominance

England is undeniably one of the strongest contenders at this year’s World Cup. But the data that we have looked at so far, doesn’t show this. This is because England’s victories against the West Indies which would have swayed their score came before the 3 month period. Let’s look at how teams have fared in periods C & D for WC 2019 in terms of their Dominance Score.

Fig. 4 PageRank comparison between periods D & C for WC 2019

While England may have been hard done by the choice of periods here and the non-Bayesian nature of my approach across periods, if we dig in, we find that England have succumbed to upsets more than the other top contenders, India & Australia, in the past year. England have lost to Scotland, Sri Lanka & West Indies in the past year. The “weakest” teams Australia & India have lost to, are South Africa & New Zealand respectively. India had one tied game against Afghanistan in the past year that can probably be called an upset as well.

But do these changes in dominance matter for the World Cup? Let’s take a look at the changes in Dominance Score from period C to period D for teams that have reached the Semi-Finals of any World Cup at least once.

If the base likelihood (prior) of a team getting into the semis is 44% (4 slots, 9 teams that have ever made it to the semis), a positive momentum in the last 3 months before a World Cup makes it 1.18x more likely. A slump on the other hands hurts only by about 12% making it 1.12x less likely for a team to make the semis.

Underdogs

We looked at teams that have seen some success, but what about rank underdogs like Afghanistan?

I built a model to predict the probability of a team reaching the semi-finals of a World Cup given their Dominance Score in the periods before that World Cup and the % change in Dominance Score from period C to period D. I built this model using all teams that have ever played a World Cup as well as with just the 9 teams that have been semi-finalists at least once, and looked at the coefficients.

I built a logistic regression model and trained it multiple times on different subsets of the data. I standardized the inputs by dividing by stddev.