Motivation

The Iowa Hawkeyes Men's basketball team has seen its fair share of ups and downs over the past 20 seasons. From NCAA tournament appearances to NIT rejections, there has been plenty of triumphs and turmoils to examine over the past 20 years. This analysis investigates some trends in the performance of the team over this time frame (1999-2019). Specifically, it seeks to shed light on a variety of trends and hunches the average fan may hold, including the belief that the team performs its worst on Sunday afternoons (Iowa City is the #1 party school after all). More substantively, this report dissects the following broader questions: How has the team performed over time? At Home versus on the Road? Against different conferences? Are there any other trends of note? All of these questions will be answered with the fairly crude, but not meaningless, method of using point margins-or Iowa points scored minus opponent points scored-and win rates (after all, winning isn't everything, it's the only thing).

Day of Week

One thing that we were interested in was how the day of the week affects perfermance. Unlike football, there is a lot of variability in what day of the week a basketball game is played in. One of our hypotheses was that the team plays poorly on Sundays compared to any other day of the week. The plot below is a visual of how the number of wins and losses changes based on the day of the week.

This graph provides for an easy comparison between the number of wins and losses and furthermore, it illustrates how many games are played for each day of the week. It is very clear that Saturday has the largest number of games and Monday has the fewest number of games. This graph is good for initial exploration but it is difficult to compare the win percentages from one day of the week to the next so a different plot will be needed. We created a graph that would allow to accurately compare win percentages on different days.

The red line moving horizontally accross the graph is an indication of the overall win percentage by the Iowa Hawkeyes. Not only was our initial hunch incorrect but the team actually plays at an above average level on Sundays. The worst two days of the week are Wednesday and Thursday. We think that after looking at this plot, betting on the Hawkeyes could be influenced by what day of the week the game is being played on.

Month

After loooking at days of the week, we wanted to explore how month of the season affects performance by the Iowa Hawkeyes. The process is very similar to how we looked at day of the week. Our initial hypothesis was that there is a "March Slump" where the Hawkeyes tend to do worse in March compared to any other month. We started with an initial bar plot showing the number of wins and losses in each month. Remember, a basketball season typically runs from November to March.

Based on this graph, we see that the number of wins is very high at the start of the season and slowly decreases. We can also see the number of losses starts small at the start of the season and increases drastically in January and February. There are only two games that have been played in April over the past 20 years explaining why the bar chart is so small for that month. It looks like the biggest magnitude of games is played in January and February. After looking at this plot, we want to explore win percentages by month more closely.

Again, the horizontal red line accross the graph represents the average win percentage accross all games. It looks like there is not just a "March Slump" but an end of season slump. Iowa Hawkeyes start every season off strong and finish below average. Knowing a little bit about the data though, this is in part because the Iowa Hawkeyes play more competitive opponents in the later half of the season.

Calendar Plot

After examining how day of the week and month of the year affect performance, we wanted to create a visualization that would include both. We thought this could best be done with a calendar plot using average point differential accross all years as the response variable. This did provide some difficulty. For example, January 1st may be on a Monday one year and then be on a Tuesday the next year. Furthermore, there is only a leap year every 4 years unless the year is divisible by 100, then there is no leap year unless the year is divisible by 400, then there is a leap year. We noticed that the point differential on "leap day" was zero so we felt comfortable not representing it in the data. As far as what day of the week to use, we decided to go with the calendar from 2018. By averaging all 20 years, we thought that this would create a sort-of block design washing out the day of the week effect but still providing a powerful visualization.

## `summarise()` has grouped output by 'Month'. You can override using the `.groups` argument.

This plot further confirms that the Iowa Hawkeyes do well at the start of the season but then finish poorly. Point differentials are generally positive in November and December but then decrease in the later half of the season. We liked this plot because it demonstrated what we noticed earlier but also provided some insight into the variability of point differential. Negative point differentials in November are rare except around Thanksgiving. Negative point differential values are rare in December except before Christmas around finals time. January and February have a lot of negative point differential averages but we do see a sprinkling of white and green giving us some insight into the variability of how well the Iowa Hawkeyes score.

Home vs Away Success

Point Differential by Location

It is no surprise that teams perform stronger at home than on the road, but how exactly have the Iowa Hawkeyes performed in this department? The figure above illustrates the point margins across each year in three different basketball floor venues: Away, Home and Neutral. Aside from the most recent season, the average point margin for home games was drastically higher than that for road and away games, averaging +10 point margin. This may mostly have to do with playing cupcake teams early in the year to get easy wins, but certainly can be explained in part by the familiarity and crowd effect being in Iowa City has on players' performances. Interestingly, but not unexpectedly, away games have the lowest mean point differential in nearly every season, with neutral floor games a still at a negative clip but a few points better.

Win Percentage By Game Location

The following chart above depicts the relative win proportions for the Iowa Hawkeyes over the 20 year span of games from 1999-2019. As can be expected, nearly every season sees a substantially higher winning percentage at home than on the road, and a higher win rate at neutral games versus road games. Additionally, when analyzing together with the time series line graph of point margin, years in which one location has the single season point margin average above the mean overall point margin tend to have the others follow suit. That is, when the performance in one year is strong relative to its specific venue, the rest tend to also be relatively strong and the team is likely having an overall strong year.

The neutral floor games have seen the greatest fluctuation season-to-season in terms of the average point margin, with 2009 being at an abysmal -20 clip and this past season in 2018 seeing over a +10 ppg margin. Most notably in the win percentages based on location, we see that there was a brutal stretch of losing with Todd Lickliter at the coaching helm from 2008-2011 as all three game locations saw near their respective minimum winning percentages across the entire dataset.

Performance by Opponent Conference

Success Rate: Big Ten versus 'The Rest'

Shifting gears to performance by conference, we unpack the performance against Big Ten teams versus the rest of the field. The alluvial plot above shows that Iowa does play most of its games against other conferences at home as mentioned in other areas of this analysis (early warm-up games against weak teams constitute a good chunk of these). Squinting deeper, we see that in home games (H) the win rate is vastly higher versus the rest of the field than against the Big Ten. In away games (A) the win rates are much more similar, although the Hawkeyes still appear to perform better when playing against non-Big Ten opponents. Finally, neutral floor games (N) are almost identical in their win rate at around 50-50 when playing against each conference. Overall, the Big Ten slate of games has been much harsher than the rest of the field for the Hawkeyes over the past 20 years.

Average Point Margin by Conference

Looking more closely at performance against conferences, we check to see the average point margin against each to get more insight on what conferences really have given the Hawks some trouble. We will just focus on conferences in which there have been a modest sample size of games played, which we will say is at least 10 games played in the last 20 years.

The above plot illustrates the win rates and average point margin for the Hawkeyes over the entire dataset against the nine most played conferences. There is clearly no struggle against the mid-major conferences of the WAC, SWAC and MEAC, as the Hawkeyes have an average point margin of +12.5 and higher and have won nearly every game versus these teams. Nearly all of the games were played against the four conferences of the ACC, Big Ten, Big 12, and MVC, and we in particular note that the ACC has been the stiffest test with just a 32% win rate and averaging a -6 point margin. The only other 'major' conference other than the Big Ten is the Big 12, which when compared to the Big Ten the Hawkeyes performed with a higher average point margin but actually won a lower percentage of games. Overall, it appears that the Big Ten provided the most success in win rate for the Hawks among the three major conferences despite winning less than half of these games over the past 20 years.

Conclusion

After looking at a few of the time trends, it is clear that Iowa Hawkeyes tend to do the worst on Wednesdays out of all days of the week. Being the "#1 party school" does not seem to affect weekend performance on Saturdays or Sundays as much as expected. In fact, it brings out our best performances. An analysis of monthly performance indicates that the season starts off very strong and tends to be worse in the last half. There are a few noticiable breaks in this pattern around Thanksgiving and finals week where the team has an uncharacteristic performance at that time of year.

Looking at the broader trend of win rate and point margin across seasons by game location, we unsurprisingly see that the Hawkeyes have their strongest performances at home, followed by neutral floor games, with away games bringing up the rear. Seasons in which they perform above the 20 year 'trend' in one location tend to be met with above average performances in other locations, suggesting an overall good season in that particular year. Inspecting performance across conferences, the win rate against the Big Ten as an aggregate is worse than against the pooled win rate against all other teams combined. Furthermore, the individual conference that provided the stiffest challenge in terms of point margin and win rate was the ACC followed by the Big 12.

Overall, we can see how visualizing the data disproved some of our hunches about the team over the past 20 years. A more in-depth analysis that accounts for strength of opponent could help further understand the Iowa Hawkeye basketball team's performance, but this initial analysis provides a solid foundation.