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We could use online data to answer some question(s) about sports and how important some aspects on an athlete determine success in their sport.

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We could do some project based off of taking basic chemical principles we have learned and applying them to something

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We are going to research NFL superstars that have and have not sustained major injuries in their career, and compare the results of their careers after they sustained injuries. We will compare and contrast players that got hurt at different positions, and determine how detrimental this was to their career afterwards. This research project will consist of a complete analysis on the statistics of these superstars and from there we will deduce how and if these serious injuries affected their career.

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Injuries are know to be very detrimental to NFL teams. Often, NFL teams’ seasons can be defined by injuries. Just looking at recent history, teams like the 2017 Houston Texans, 2017 Green Bay Packers, and 2016 Minnesota Vikings are teams that had legitimate Super Bowl potential and fell out of the playoffs just due to injuries to Deshaun Watson, Aaron Rodgers, and Teddy Bridgewater and Adrian Peterson. Even the Super Bowl Champion Philadelphia Eagles lost their starting quarterback Carson Wentz to an ACL tear in Week 14. In addition, individual players’ careers can be completely derailed by injuries. Injuries are so devastating in the NFL that the average career length is only 3.3 years, with running backs only lasting 2.57 seasons(1). With this all in mind, it is important to understand how injuries impact player’s careers, particularly the superstars of the NFL. They are often the difference-makers on how much success a team will have(2), and understanding how effective they will be after returning from injuries is paramount for front offices to understand what their strategy should be in terms of trading and draft choices. As of now, some is known about how long players are expected to be out with injuries, but little is known about how these players’ careers project after major injuries. Some injuries, such as concussions are known to not strongly affect a player’s on-the-field performance after they recover, while other injuries, such as ACL tears or bone breaks, can seriously change the course of a career. It’s important to figure out how much of an effect an injury is expected to have on a player, in order for a front office to determine the best course of action with regards to that player. Our research will look into approximately 15 NFL superstars, players we’re considering made at least 2 Pro Bowls, and see how major injuries affected their careers. We will do this by first identifying the players we will use, then gathering data on them and their injury or injuries. We will then look at their statistics for the seasons before the injury, directly after the injury, and far down the road after their injury to see how the injury actually impacted them. These statistics will include basic numbers like yards and touchdowns, and more advanced statistics like QBR and yards per catch. In addition, we will look for more advanced context into other variables for why they had the numbers they did, such as the success of the players around them. This way, we can conclude how much injuries impacted their success versus their teammates. Through all of this, we hope to reach a conclusion on how much NFL superstars are impacted by injury.

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To explore and answer whether or not injuries have a significant on an athlete’s performance. We start with a null hypothesis that states there is no correlation between the injury and the players statistics and performance. Afterward we develop an alternative hypothesis that supports the notion that injuries are in fact a contributing factor to why NFL superstars end up doing worse in games. The first step to proving our alternative hypothesis correct requires selecting a group of fifteen NFL superstars, five of each of the following positions (quarterback, running back, and wide receiver), that have been injured severely. Our definition of a superstar is someone who has participated in a pro bowl for two or more years. We will use Pro Football Reference (3) to select the athletes that follows our criteria: an athletic superstar who received an injury. This website enables us to search for athletes who participated in various pro bowls and gives access to received injuries by our players with ease. We will select the following players: Quarterback Peyton Manning, Running Back Adrian Peterson, Quarterback Tom Brady, Quarterback Donovan McNabb, Quarterback Tony Romo, Wide Receiver Jerry Rice, Running Back Emmett Smith, Wide Receiver Terrell Owens, Running Back Marshall Faulk, Wide Receiver Isaac Bruce, Quarterback Carson Palmer, Wide Receiver Julio Jones, Wide Receiver Michael Crabtree, Running Back Arian Foster and . After the selection process, we will again use Pro Football Reference (3) to analyze these player’s statistics. The next couple of steps will involve performing a two sample T test which essentially tests whether two means are equal. We are looking at a paired set of data, meaning there is a one to one connection between variables of two different samples. For example, if Quarterback A made 32 touchdowns in one particular season, received an arm injury, then went on to make only 18 touchdowns the next season, we would make the injury our variable and establish whether or not there is a connection between our two different samples which are the amount of touchdowns. We begin our analysis by writing down and recording all the important statistics such as QBR, rushing passing yards, yards per attempt, completion percentage, and touchdowns for each of our selected quarterbacks, and rushing yards, receiving yards, touchdowns, and yards per attempt for our selected wide receivers and running backs when they were in their prime with record breaking and newsworthy performances in game all before their injuries.  It is important to see how well these athletes performed prior to their injury because we will be able to compare the before statistics to their statistics after they were injured to deduce the effects of said injury. Next, we take a look at which injuries each of our athletes suffered through and research their effects on the human body using Mayo Clinic (4), a health system with many campuses around the U.S. This source will allow us to understand how common injuries such as a concussion, torn ACL, or even a leg sprain have an effect on the athlete’s performance and career, regardless if they went on to continue playing games or quit the NFL, in order to gather reasoning if our alternative hypothesis is proven correct. Lastly, we will analyze the statistics of our selected athletes using footballreference.com much like our research for the statistics prior to the injury. Our final statistical analysis involves writing down and recording all the important statistics such as QBR, passing yards, yards per attempt, completion percentage, and touchdowns for each of our selected quarterbacks, and rushing yards, receiving yards, touchdowns, and yards per attempt for our selected wide receivers and running backs. However, this time, our statistics are from the years after they were injured and recovered. Now that our research on all of the statistics are finished, we will compare and contrast the before and after for our players to deduce whether or not the injuries they suffered through had a significant impact on how well they played. Utilizing the two sample T test is now essential. After gathering the data before and after the injury, we will try to deduce whether or not there is a one to one connection between two samples and our variable. Each player will have two samples of data for each of their statistics. For example, quarterback A has two samples: sample A, before the injury, and sample B, after the injury, of each of the following statistics: QBRs, touchdowns, passing yards, yards per attempt, and completion percentage. Due to our gathered data, we now know how these athletes performed at the peak of their career, before the injury, so with the assessment of the two sample T test, we will be able to identify whether or not they were truly affected by the severe injury they received in their career.

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To explore and answer whether or not injuries have a significant on an athlete’s performance. We start with a null hypothesis that states there is no correlation between the injury and the players statistics and performance. Afterward we develop an alternative hypothesis that supports the notion that injuries are in fact a contributing factor to why NFL superstars end up doing worse in games. The first step to proving our alternative hypothesis correct requires selecting a group of fifteen NFL superstars, five of each of the following positions (quarterback, running back, and wide receiver), that have been injured severely. Our definition of a superstar is someone who has participated in a pro bowl for two or more years. We will use Pro Football Reference (3) to select the athletes that follows our criteria: an athletic superstar who received an injury. This website enables us to search for athletes who participated in various pro bowls and gives access to received injuries by our players with ease. We will select the following players: Quarterback Peyton Manning, Running Back Adrian Peterson, Quarterback Tom Brady, Quarterback Donovan McNabb, Quarterback Tony Romo, Wide Receiver Jerry Rice, Running Back Emmett Smith, Wide Receiver Terrell Owens, Running Back Marshall Faulk, Wide Receiver Isaac Bruce, Quarterback Carson Palmer, Wide Receiver Julio Jones, Wide Receiver Michael Crabtree, Running Back Arian Foster and . After the selection process, we will again use Pro Football Reference (3) to analyze these player’s statistics. The next couple of steps will involve performing a two sample T test which essentially tests whether two means are equal. We are looking at a paired set of data, meaning there is a one to one connection between variables of two different samples. For example, if Quarterback A made 32 touchdowns in one particular season, received an arm injury, then went on to make only 18 touchdowns the next season, we would make the injury our variable and establish whether or not there is a connection between our two different samples which are the amount of touchdowns. We begin our analysis by writing down and recording all the important statistics such as QBR, rushing passing yards, yards per attempt, completion percentage, and touchdowns for each of our selected quarterbacks, and rushing yards, receiving yards, touchdowns, and yards per attempt for our selected wide receivers and running backs when they were in their prime with record breaking and newsworthy performances in game all before their injuries.  It is important to see how well these athletes performed prior to their injury because we will be able to compare the before statistics to their statistics after they were injured to deduce the effects of said injury. Next, we take a look at which injuries each of our athletes suffered through and research their effects on the human body using Mayo Clinic (4), a health system with many campuses around the U.S. This source will allow us to understand how common injuries such as a concussion, torn ACL, or even a leg sprain have an effect on the athlete’s performance and career, regardless if they went on to continue playing games or quit the NFL, in order to gather reasoning if our alternative hypothesis is proven correct. Lastly, we will analyze the statistics of our selected athletes using footballreference.com much like our research for the statistics prior to the injury. Our final statistical analysis involves writing down and recording all the important statistics such as QBR, passing yards, yards per attempt, completion percentage, and touchdowns for each of our selected quarterbacks, and rushing yards, receiving yards, touchdowns, and yards per attempt for our selected wide receivers and running backs. However, this time, our statistics are from the years after they were injured and recovered. Now that our research on all of the statistics are finished, we will compare and contrast the before and after for our players to deduce whether or not the injuries they suffered through had a significant impact on how well they played. Utilizing the two sample T test is now essential. After gathering the data before and after the injury, we will try to deduce whether or not there is a one to one connection between two samples and our variable. Each player will have two samples of data for each of their statistics. For example, quarterback A has two samples: sample A, before the injury, and sample B, after the injury, of each of the following statistics: QBRs, touchdowns, passing yards, yards per attempt, and completion percentage. Due to our gathered data, we now know how these athletes performed at the peak of their career, before the injury, so with the assessment of the two sample T test, we will be able to identify whether or not they were truly affected by the severe injury they received in their career.

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We could potentially test the water at different locations on campus for their pH and levels of chemicals like lead and copper. With this we can possibly pin point the sources of why some areas on campus have water with more acidity. We can conclude which places on campus students should be drinking water from.

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We could potentially test the water at different locations on campus for their pH and levels of chemicals like lead and copper. With this we can possibly pin point the sources of why some areas on campus have water with more acidity. We can conclude which places on campus students should be drinking water from.

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As of the beginning of the research day on 11/5, I have gathered the data for Jamaal Charles, Adrian Peterson, Terrell Owens, and Jerry Rice. My goal for today is to finish the rest of my data gathering, which includes 3 more receivers plus the data for Robert Griffin III. Next week, I will do mini case studies for the players I gathered data for plus a larger case study on Robert Griffin III.

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During the lab, I gathered the statistics and injury types for Peyton Manning, Jerome Bettis, Adrian Foster, and Marshall Faulk. Likewise, I completed the quarter back rating statistic for Carson Palmer and Donavan MccNab as I had completed their statistics prior to today, with the exception of the QBR. All statistics gathered were taken from profootballreference.com

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I finished my research for the day. Here are the players I did on a day-by-day breakdown:

October 27th:
Tony Romo
Tom Brady

October 29th:
Donavan McNabb
Carson Palmer.

November 5th:
Peyton Manning
Jerome Bettis
Adrian Foster
Marshall Faulk.

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I finished my research for the day. Here are the players I did on a day-by-day breakdown
October 22:
-Adrian Peterson
October 29:
-Jamaal Charles
-Jerry Rice
-Terrell Owens
November 5:
-Isaac Bruce
-Julio Jones
-Steve Smith Sr.
-Robert Griffin III

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https://docs.google.com/spreadsheets/d/1AOX3BFXTp26Ge1NdFtfBbO9gJSgbSm9OjEjjk2uPiyI/edit#gid=0
This is the link to our spreadsheet, as a screenshot was not possible

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I spent an extra hour this week researching runningbacks to see if there were any more we could use, and found Marcus Allen. I gathered the data on him and added it to the spreadsheet.

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Before submitting the rough draft, to add more to our results section of the paper, I researched more injuries on  a few more NFL athletes so that tomorrow during lab I can complete the research on injuries and begin to analyze more data. 

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Before submitting the rough draft, to add more to our results section of the paper, I researched more injuries on  a few more NFL athletes so that tomorrow during lab I can complete the research on injuries and begin to analyze more data. 