Saturday, October 29, 2016

Passing Yards For Aaron Rodgers in The 2016 Season (Aka Monte Carlo Models for Aaron Rodgers)

In a post earlier this year, we tried to figure out how many passing yards Peyton Manning would put up if he were to return to football for one game. We answered this question using the techniques for probabilistic forecasting of single items. What if we were to try and use probabilistic forecasting techniques to figure out an entire season's worth of performances. We have already discussed in earlier posts that forecasting multiple items cannot be done by simply forecasting single items multiple times (well kind of yes and no...). This means we have to reach into our toolbox to retrieve our preferred technique for predicting multiple items - Monte Carlo simulations. We are also going to change our subject to a more relevant one. As we are almost midway through the current season, let us pick an active player and see what predictions we can make concerning his performances for the remainder of this season. We are going to pick Green Bay Packers' Quarterback Aaron Rodgers as our subject for these predictions. We will compare how well he has done in comparison to our predictions so far in the season and how well he will do in the remainder of the season. Answering how many yards a quarterback, especially one as prolific as Aaron Rodgers will throw for in a season can be a daunting task. As daunting as predicting the end date for a software project.

The quality and accuracy of projections coming out of Monte Carlo depend squarely on the model being used as input for the projections. The first decision we have to make is which past data points do we use as inputs to our Monte Carlo simulations. Aaron Rodgers was drafted in 2005 and took over as starting quarterback for the Packers in 2008. That means we can safely ignore all games he participated in before 2008. Also, clearly the team and the system under which Rodgers plays has changed quite a bit since 2008. For this reason, we can limit ourselves to the last 5 seasons. We are also going to exclude any games where Rodgers left the field injured and could not complete the game. That leaves us with 77 games, including the ongoing season. We will give each of the performance in these games equal weight for our simulations. Which means, that for each of the set of games we are trying to predict, Rodger's performance is equally likely to be similar to any of the past 77 games. 

Now that we have narrowed down our input data set to what we believe is a representative range for the upcoming games, Let us see how the results that we get from MonteCarlo compare to those we would have gotten by straight averages. For the seasons from 2011-2015, for the games that we are considering as input, The average for Rodgers was about 276.75 yards per game. If we were to make predictions based on this average, we would say that for the entire season, Rodgers will throw for 4428 yards this season. Also, for the first six games of the season(Games completed at the time of writing of this article) Rodgers, based on average would have accomplished 1660 yards.

Testing Predictions Against Past Games

Let us run the MonteCarlo simulations for the first 6 games of the season, assuming that these games have not yet taken place. In other words, let us pretend it is the beginning of the season and we are trying to predict how many yards Rodgers is going to throw for in the first 6 games.
We get the following results -

Predictions For The First 6 games of 2016 Season
15% Certainty1835 yds
30% Certainty1750 yds
50% Certainty1661 yds
70% Certainty1570 yds
80% Certainty1524 yds
85% Certainty1484 yds

These results can be interpreted as confidence ranges for Rodger's performance. What Monte Carlo is telling us is that we have an 85 percent confidence that Aaron Rodgers can throw for at least 1484 yards, we have a 50% certainty that he can throw for 1661 yards, 15% certainty for 1835 yards, and so on. As we see, the higher the confidence level the lower the number of yards we can predict.  So far, Rodgers has thrown for 1496 yards, which is 164 yards(or more than the total yards in a game vs Arizona last year) off. The 85 percent certainty number from Monte Carlo, on the other hand, is off by 12 yards, or for a prolific quarterback like Aaron Rodgers, the yards gained from one pass. At the beginning of the season, Rodgers (and his agent) can use this information to set expectations for the season. Coaches can use this information to plan for the season and decide how much importance they put on the run game and on their defense based on the level of confidence/risk they want to assume.

These numbers also provide us some validation for our present model and give us another bit of information. The fact that we are getting such a close prediction at the 85% certainty mark, tells us that Aaron Rodgers is performing at a lower level than what he is capable of. 85% certainty can by equated to saying that Rodgers is performing at 15% of his maximum potential and  about 30% of his median potential.

Predicting The Rest Of The Season

Using the same methods we used to predict the first six games, we can attempt to predict the remainder of the season. We will make the 6 games that have already happened this season as a part of our model. Running the model through Monte Carlo for the remaining ten games of the season gives us the following results - 

Predictions For The Last 10 games of 2016 Season
15% Certainty2948 yds
30% Certainty2849 yds
50% Certainty2733 yds
70% Certainty2615 yds
80% Certainty2549 yds
85% Certainty2516 yds

Since Rodgers has already thrown for 1496 yards this season, we can try to figure out the number of yards the Packers Quarterback will rack up for the season -

Predictions For The Entire 2016 Season

15% Certainty4444 yds
30% Certainty4345 yds
50% Certainty4229 yds
70% Certainty4111 yds
80% Certainty4045 yds
85% Certainty4012 yds

If we had taken the average yards per game from the previous 5 seasons(276.75 yds/game), and used that as a projection for the 16 games in this season. We would have predicted that Rodgers would pass for 4428 yards this season. Based on the simulations we have run so far, it seems that Rodgers can only hit that mark at the rate predicted with 20% certainty. For a quarterback that is operating below par and maybe inspiring a lower level of reliability, we should use a number at the other end of the scale if we were forced to pick a number. Using the 85% certainty number, which is 4012 yards, is probably a much safer bet to make and plan for, whether you are Rodgers, his coaches, his agent or someone placing bets in Vegas.

A Smarter Model

 Our model so far has been pretty straightforward. Assume that Aaron Rodgers will perform in future games in a manner similar to one of the past 77 games. The beauty of this model is the simplicity of it. It requires almost zero football knowledge to understand it. All it needs us to understand is that yards are a unit of measurement of productivity for a player in a football game. We do not have to understand any rules, strategies or other measures and metrics regarding football. What if we could come up with a smarter(maybe better) model that still maintains this simplicity. 

"Everything should be as simple as possible, but no simpler" - Albert Einstein

Let us run the same simulations using a model where we considered the opponent that the Green Bay Packers are up against. What that means for us is that as we try to figure out future performances, we will not randomly select from the past 77 games. We will instead simulate from full games that Aaron Rodgers has played against the particular opposition the packers up against. In essence, all games against Chicago Bears will be sampled only from prior games against Chicago Bears. Using this model we get the following results for the first 6 games of 2016 -

Predictions For The First 6 games of 2016 Season

15% Certainty1717 yds
30% Certainty1641 yds
50% Certainty1554 yds
70% Certainty1481 yds
80% Certainty1432 yds
85% Certainty1402 yds

These predictions are all lower than the predictions of the simpler "random" model. In fact, in this case, the actual yardage of 1496 has a 66% certainty or saying that Rodgers is performing at 34% of his maximum potential (as opposed to 15% from the "random" model) .Why is this model giving us more pessimistic results? Why are the same 1496 actual yards interpreted as different levels of performance for Aaron Rodgers? Taking a closer look at the data answers the question for us. Since 2011, Rodgers' only 400+ yard games have come against Denver, Washington, and New Orleans, teams that are not on the schedule for 2016. This means that when we simulate the games for 2016 based on the opposing team, these games do not get considered at all. This lowers the projections for the group of games we are simulating for.

The projections for the remainder of the season and the overall projections are as follows - 

Predictions For The Last 10 games of 2016 Season

15% Certainty3046 yds
30% Certainty2943 yds
50% Certainty2854 yds
70% Certainty2761 yds
80% Certainty2704 yds
85% Certainty2675 yds

Predictions For The Entire 2016 Season

15% Certainty4542 yds
30% Certainty4439 yds
50% Certainty4350 yds
70% Certainty4257 yds
80% Certainty4200 yds
85% Certainty4147 yd

This model for Monte Carlo suggests that Rodgers can be expected to do better than the predictions from the "simple" model from the rest of the season. The Packers' Quarterback has historically performed better against the teams the Packers are going to play in the remaining of the season as compared to those that they have played in the last 6 games. This is also borne out when we look at averages - Against the first 6 oppositions, Rodgers averaged 263 yds/game as opposed to 270 yds/game against the next 10 opponents.

In Summation

In summation, we can conclude that Monte Carlo predictions (probabilistic forecasts) give us a much better chance of answering the question regarding multiple game performance than averages do. Based on our level of confidence/risk tolerance, we can choose the certainty level and plan accordingly. We also see that different models give us different results. We have to figure out the best models that fit the reality of our situation, but at the same time not make them too complex or specialized. As Albert Einstein said - "Everything should be as simple as possible, but no simpler".

Monday, October 10, 2016

Donald Trump : The Lean-Agile Candidate

This article is not an endorsement of Donald J Trump. Instead, this is an analysis of how well the Trump campaign has utilized many of the techniques and strategies of Lean and Agile to run a very successful campaign under some very trying conditions. The campaign, since its beginning, has had a shoestring budget, a mercurial candidate with no prior public office experience, very little core establishment support and lack of a broad, informed policy platform. Despite all these hindrances, Donald Trump, has not only beaten out a crowded Republican field but also remained competitive till late in the election cycle against an established political Titan, Hillary Clinton.

Lean Startup

Donald Trump's candidacy was considered a joke for quite some time. This gave Trump the ability to take risks to make a mark for himself. In the beginning of his campaign, he made many outlandish statements. Many of which would easily have rendered him unelectable if he was being taken seriously. His immediate strategic focus though was to make some noise and gain notoriety. He is a startup in the field of established players. Doing what everyone else does is not going to help him separate himself in a field of 17 contenders for the Republican nomination. The same applies to new products. Yes, the table stakes (In this case having a pulse and enough backers to launch a bid) are necessary, but not enough to be successful. You have to stand out, even if it is in an unorthodox manner to gain market share. Breaking the mold and having a distinctive appeal is critical for any startup.

At this point, it is not just the primary voters that take note of Donald Trump, but his outlandish statements start bringing in a lot of media attention. The amount of free air time that Donald Trump's comments and Trump as a guest himself gets from the various networks greatly exceeds the paid and free airtime for any of his competitors. This goes a long way in cementing the Trump political brand. Trump knows that the voters in the Republican primary are not fans of the media. Hence, while he feeds soundbites to the media, he also chastises them for unfair coverage. This becomes a consistent theme for the remainder of the Trump campaign. As a new product, it is important to establish a brand with your customers. Use all avenues available to remind your customers of your brand and how it stands out.

This beginning and most of the rest of the campaign seems to have been run using Lean Startup principles almost by the book. Trump repeatedly employs the Build-Measure-Learn loop to not just figure out the right things to say, but also to create and adjust policy positions. The campaign guides its steps by observing the customer reaction and understanding them first hand, rather than through pollsters and policy experts.

Limiting WIP

Trump has been, until recently very focused on the immediate strategic direction. During the primaries, Trump campaign had multiple hurdles ahead of them. Instead of tackling all 16 of his opponents at once, Trump goes after them one at a time. While others are not taking him seriously, he starts by discrediting the most lucrative target. The establishment heavyweight Jeb Bush. Trump repeatedly calls him weak and makes sure that Bush is known as the establishment candidate. This is a great strategic direction to take as it is potentially the easiest and most lucrative. Notably, he does not go after the other candidates at this time. He limits his WIP to one strategic direction at a time. Trump does not have the resources to spread his attacks out. This forces him to be lean. repeating the same message about one candidate over and over helps him get the best results against that candidates with his customers.

The Trump campaign in time, shifts focus to Marco Rubio, John Kasich, and eventually Ted Cruz in order to eliminate the competition one at a time. He picks on them one by one and brands them in ways that would hurt them with republican primary voters.  This entire time Trump's focus was one of his Republican opponents and not Hillary Clinton. He made wildly unpopular statements, but these were unpopular overall, not amongst the voters that would show up to vote in the republican primaries. Trump proved that limiting your WIP works at all levels, especially at the strategy level in a lean organization.

Feedback Loops

Most political campaigns thrive on feedback loops. They adjust as they get more information through polls and media feedback. The Trump campaign has taken this to the next level. Trump seems to be deliberately creating these feedback loops using rallies and social media. There are elements of Lean UX, Dev-Ops and Continuous Delivery present in the implementation of these feedback loops.

Lean UX

As opposed to other candidates that spend long hours coming up with sound bites and attack lines, Trump's campaign spends very little time doing such analysis. Trump tries out every new sound bite with audiences, both live and on twitter, till he finds the one that sticks. The campaign is consistently pursuing the "linguistic killshot" as Scott Adams (of Dilbert fame) puts it. Lying Ted, Little Marco, Weak and Low Energy Jeb Bush and even Nice Ben Carson were all effective branding of Trump's opponents that hurt their campaigns. These "linguistic killshots" were not a result of hours and days of research. The Trump campaign took multiple ideas straight to the rallies and carried on the ones that gained traction. The users help shape the experience rather than simply being subjected to it.  Instead of spending days analyzing and doing research, Trump was getting ideas out first and getting feedback. When you are trying out new user experiences, the easiest way to validate which ones work is to actually put them in front of your customers. 

Linguistic killshot: An engineered set of words that changes an argument or ends it so decisively, I call it a kill shot. One of the ones Donald Trump used was referring to Bush as a "low energy guy" or Carly Fiorina as a "robot" or Ben Carson as "nice." - Scott Adams

Dev-Ops and CD

Much is made of Trump's late night/early morning tweets. These are often the more "fiery" and controversial tweets that Trump puts out. The Trump campaign has realized the power of social media and utilized in a lot more effectively than the Clinton campaign has.Trump has been running his campaign with a lot less operating cash than his opponents. In May, the Trump staff consisted of 69 staffers as opposed to 685 staffers for Hillary Clinton. This means that Trump has to find new and better strategies for getting the word out. Trump delivers his messages directly to his customers, early and often. Early morning tweets are not just seen by his followers when they wake up, but due to their controversial nature, re-tweeted and replied to by thousands of folks. What is even more important is that the early morning news and talk shows pick these up and talk about them for hours. Trump is not just the engineer of the messages but also does the deployment of it out in the field. He delivers early and often. By doing this, he is able to shape the conversation for the day without making the rounds of the news and talk shows.

According to New York Times, in March, Trump had received almost 2 billion dollars of free media coverage due to his continuous delivery of unique messages.

A common charge leveled against Trump is that he "takes the bait" and cannot resist responding to every charge leveled against him. This might be a valid charge and Trump might have little control over his instincts. The tendency to do this, though, still provides all the same benefits. By using multiple rapid fire responses, the campaign is able to identify which ones resonate with the voters and implement those in detail. Trump being the engineer and the deployer of these cuts down the amount of time it takes to run these responses by the "end-users". The campaign also does not waste time trying to craft the perfect response and allows the "customers" to choose which response the campaign spends time on, in order to perfect it.


The trump campaign, (until very recently) has been the definition of anti-fragility.
Antifragility is a property of systems that increase in capability, resilience, or robustness as a result of stressors, shocks, volatility, noise, mistakes, faults, attacks, or failures.
Software developers anticipate volatility and shocks to the system in order to make the system perform better under these forms of stress. A great example of this is Netflix's Chaos Monkey that turns off services randomly to make sure the system is built to handle the stress. Trump is his own campaign's Chaos Monkey. He is unbridled in his speeches, tweets and personal interactions. His team has known this from day one. Every campaign manager and media relations person on his team has become an expert in the art of the spin. This means two things. First, it does not matter what Trump actually says. The media and the political elite might sneer at his remarks, but Trump's team has a lot of practice in handling these situations. They make every remark, that would otherwise sink a campaign, into a positive for the country. Second, it allows Trump to keep running his experiments with words. The media and the establishment up in arms against him is an anticipated stressor. It makes his image as the anti-establishment, outsider more robust with every attack. The Trump campaign is the mythological Hydra, which becomes stronger when it is attacked.

Small Batches

This political cycle has made it hard to concentrate on actual policy matters. While most candidates went into the race with filled out platforms and explicit positions on all the major issues, Donald Trump did no such thing. He went about making his policy explicit in small batches. He first released  his position on immigration in August 2016. Even in this case, most of the plan was kept flexible to account for the feedback from the voters. The Trump campaign has shifted positions based on what seems to work with the public at their rallies, rather than what the experts think. This is the exact opposite of a well thought out, heavily analyzed political platform. Rather than having a set in stone "product roadmap", the Trump Campaign releases information on policy initiatives in small batches so that they can be easily consumed and future initiatives adjusted as needed. 

Trump is executing Build-Measure-Learn, while everyone else is doing large upfront analysis. Trump's campaign is agile while most others are living in the traditional Waterfall world. Trump is able to make repeated policy shifts without people taking much notice because he makes these shifts in small batches by changing little details as opposed to having to stay stuck on a pre-defined and committed to a platform.

The Lean-Agile Candidate

Donald Trump is far from an ideal presidential candidate. He has great flaws that he seems to escape just as they catch up to him. Many times, he comes out stronger than before because these flaws help him show his anti-fragility. The Trump campaign has done a great job of tapping into the voters directly and making an otherwise improbable candidate into a strong presidential contender. The strategies and tactics used by the campaign, whether knowingly or not, bear great resemblance to the Lean and Agile principles that we encourage teams and organizations to adopt. Of late, Trump's old words have come back to haunt him. Such deep-rooted flaws are probably beyond the anti-fragile ability of his campaign. However the race ends, this lean and agile campaign has probably changed the world of political campaigns for years to come.