Sports & Fantasy Sports Blog

Author: Bill Jones Introduction Once upon a time in the vast realm of fantasy football, a passionate adventurer set out on a thrilling quest. Armed with a deep love for the game and a fascination with data science, he embarked on an exhilarating journey to unlock the hidden secrets of best ball fantasy football. As he ventured deeper into the realm of best ball content, a dynamic data competition caught his attention: Best Ball Data Bowl. Intrigued by its potential, he delved into the depths of data hoping to unearth valuable patterns and insights that had remained concealed from the naked eye. Join me on this exhilarating journey as I attempt to the mysteries of best ball and rewrite the playbook for success.  Thanks for bearing with me while I had a bit of fun with chatGPT, now back to talking normal… BBM Basics For those who may be less familiar with Best Ball Mania (BBM) by Underdog, it’s a unique and immensely popular fantasy football tournament. Best ball is a fantasy football fo

Author: Bill Jones Introduction Hey football analytics fam, welcome back to my blog! Today, I want to share another one of my studies. I recently saw on twitter a scout visual showcasing the statistic drop percentage for quarterbacks, but I have always considered drops to be a garbage figure, so this caught my attention. Intrigued by this being shown and my preconceived notion being incorrect, I embarked on a quest to uncover the truth through using some basic data science techniques. Together, we'll peel back the layers of this statistical onion and uncover the hidden truths that lie within the realm of drops. Let's kick off this study and get into the data. Ground Rules Before we jump back into the analytics, I would like to let the readers know the ground rules I will be playing with. The data used for this analysis was obtained from Pro-Football-Reference for 2018 through 2022. Additionally, for any quarterback to be included in the analysis they must have had 200 passing a

Author: Bill Jones Introduction Welcome back to the third installment of my blog series where I perform an analytical review for tight end fantasy performance from 2022. In this blog, we will be exploring the relationships between data points that simple logic would assume are closely linked, such as rushing yards and rushing touchdowns, passing yards and passing attempts, and more. We will be using visualization tools to help us determine if these relationships actually exist and if there are any outliers or anomalies in the data. From there I will comment on what this might mean about their performance in 2023. I'm particularly excited about this blog, as we start to get a little deeper into the realm of data science-y things. This blog touches on topics of correlation and anomalies, and how they can be used when evaluating player performance. These are easy concepts and tools which help towards a comprehensive understanding of what happened in 2022 and what could be in 2023. Let

Author: Billy Jones Inspiration: Paul Sabin (@SabinAnalytics/SumerSports) Introduction Welcome back, football analytics fam! I’m excited to finish this series where I analyze Justin Fields' production compared to other elite rushing quarterbacks over their first two years. Now, it's time to shift the focus to the future and look into what Year 3 might hold for this talented quarterback. As I analyze his performance and potential, I'll explore how the other quarterbacks in our study performed in year 3. So, let's dive in and take a final look at what could be in store for Justin Fields in Year 3! Ground Rules Before we jump back into the analytics, I would like to remind the readers of the ground rules we will be playing with. The data used for this analysis was obtained from NFLFASTR for 2000 through 2022. For each of the quarterbacks in the analysis we will be looking at only their first 3 years in the NFL. The quarterbacks included in the study are the top 10 rushing

Author: Billy Jones Inspiration: Paul Sabin (@SabinAnalytics/SumerSports) Introduction Welcome back to the blog! I’m excited to continue this series where I analyze Justin Fields' production compared to other elite rushing quarterbacks over their first two years. In the first two parts I discussed Justin Fields’ rushing production and highlighted his extremely high scramble and sack rates. Quarterback runs are fun and all that but we all know this is a passing league and that’s where games are won. In this part I'll shift my focus to Fields' passing production compared to the other players in this study. To do this I am going to use some more traditional statistics but also include some of the newer ones that you may not have of heard but are growing in popularity. Thank you for those that have followed along with this series and let's dive back into the world of football analytics! Non-Traditional Statistic Explanation Per PFF , “Expected Points Added (EPA) is a measur

Author: Bill Jones Introduction Hey fantasy football analytics fam, welcome back to my blog! With the NFL draft finished, many of us are eagerly anticipating the influx of new talent that will soon hit the league. For those of us that play dynasty fantasy football, it’s important to start thinking about how we can evaluate these rookies, if you haven’t already, and formulate an opinion about their potential impact on your fantasy teams.  Over the past few weeks, I have thoroughly enjoyed reading many of my colleagues’ models and ranking systems that have been published leading up to the draft. In doing this research I had one recurring frustration in most analysts ranking processes. This frustration was that almost all of them (which were transparent about their process) included projected draft capital as a data point for their prediction. To me the inclusion of this data point felt a bit odd as the NFL draft capital is essentially already a ranking that bakes in all the other data po