Ib Math Studies Ia

In this project, many different statistics were used to try and predict the winners of the NCAA March Madness tournament. To do this, statistics were tested from the previous year to see if they moderately correlated to winning games. When a stat is moderately correlated, that means it relates to winning. Using a scatter plot, a graph that gives a visual of whether or not a stat is correlated, correlation coefficients were found for each stat. The correlation coefficient is a decimal that shows if a set of numbers is moderately correlated. After finding stats that were the closest to being moderately correlated, a metric was put together that used the best stats to determine which teams will win games. The stat that was the closest to being moderately correlated was the turnover ratio of the teams. Another stat that was almost moderately correlated was RPI, or Rating Percentage Index. This stat uses a team’s win percentage, their opponents’ win percentage, and their opponents’ opponents’ win percentage to rank teams. Also, missed field goal percentage was a stat that was used.

A big part of what most people will notice right away if they watch NBA and College basketball is the officiating. In the NBA, as soon as a team reaches 5 fouls in a quarter the opposing team gets two foul shots for every foul after that, whether it’s a shooting or on the floor fouls. Once the quarter ends, the team's fouls will reset, but not personal fouls. This is much simpler than how fouls work in College basketball. Instead of quarters college basketball has halves. So, in each half as soon as one team reaches 7 team fouls the opposing team gets 1 fouls shot for every foul from there on out. This happens until the team is to reach 10 team fouls, from this point on the opposing team gets 2 fouls shot for every foul. Once a half is over then the team' fouls will then reset back to zero. The biggest difference most people will notice is the fact that in the NBA a player can get up

For offensive tackles broad jump and vertical leap showed the greatest positive relationship because offensive tackles need those skills in order to perform their tasks in the field and if someone have a high broad jump it means that score in a vert leap also going to be positively correlated.

Orlando: defensively, also rank 18th, on average allowing 101.6 points per game. The Los Angeles Lakers offensively, rank 18th in the NBA, on average scoring 102 points per game. Defensively; the Los Angeles Lakers rank 27th in the NBA allowing 109.3 points per game.

It does seem like some important information is missing. For instance, how can you tell who has better skills based on the number of years playing the game? After playing for five years in the league, none of them may be an all-star. Perhaps the kid who started playing at age 11 is a better player than the three who have five years of experience.

During the 2012-13 season, his minutes were increased to 35.15 per game, and he responded by averaging 13.3 PPG with 6.3 RPG. Through 54 games with the Sixers in 2013-14, he upped his average during to 17.4 PPG.

Tommy was always excited for the first day of practice. For the last three years he has dribbled his way to greatness. Tommy was now a senior at Belleview Heights High school his coach had him down as a 6’0” - 165lbs point guard, but he knew only on a good day he was even 5’11”. . But that’s what coaches do they bump up all the players’ heights and even weights on the stats, hoping

A heavier or taller football team could potentially result in a slight advantage when going up in a contest against a smaller player as they have a greater chance of overpowering them. Another factor could simply be the talent and experience of players in a team. If one team has more experienced and talented players than the other, then they will most likely perform better. Although talent would be difficult to measure, experience could be explained by having one player who is 30 years old, but has only played 20 games, whereas a 25 year old player may have player 60 games in their career. The Pythagorean expectation also does not take into account potential injuries that may occur throughout the season. Even though a team may have performed well last season, they may have had several players who had injured themselves, which could in turn affect the teams performance. All these factors could have potentially affected the accuracy of the Pythagorean expectation formula, which highlights how despite the strong correlation between the mean age of the team and the number of games won, this is not the only cause as there are other factors that could have also influenced the

It stands to reason that with a hoop placed 10ft off the floor, anyone who wants to put a ball through an 18in basket would need to be tall. However, throughout history, there have not been many people, men or women, that could reach that tall. This poses a unique challenge of having a person who can at least jump and throw a ball accurately enough to have the ball sink through the hoop. With that, there still needs to be some tallness to the person jumping and throwing the ball. Most people can only jump a few feet up into the air, and some are not even able to make it that high. So how tall does a basketball player need to be?

Professional basketball is a game of giants. Players ranging all the way up to 7 feet and higher battle on hardwood to place a ball through the hoop. In the United States, the average height for males comes in at around 5 foot 10 inches. Existing in a seemingly alternate universe, basketball players even 6 foot or 6 foot one are considered short. Floating inside of this universe of massive men, Isaiah Thomas challenges the norm. Could a 5 foot 8 player possibly survive, let alone thrive inside of the universe of professional basketball? Isaiah Thomas resoundingly answers this question in the affirmative. Coming in at 5 foot 8, 185 pounds, Thomas’ miniscule height, even for an average man, leaves him gazing up at the opposition. Drafted with

A potential issue arises from talent identification as it is not an exact science and often subjective, and with research showing that physical and biological attributes alone do not have great effect on player skill in team ball sports (Burgess., 2010). The use of these should be limited or only used as a compliment to the broader decision-making process. However talent identification programs are heavily influenced by these measures (Abbott.,

To compare the results of the ILP to real games, we used statistics from the Golden State Warriors, the Dallas Mavericks, the Indiana Pacers, the Cleveland Cavaliers, and the Los Angeles Lakers highest scoring game of the 2016-2017 season. This data is available on the NBA website under "Stats" filtered by "Box Scores", dates, and quarters. We used these statistics to get the number of 2PA and 3PA per quarter j and compared to the results of the ILP.

In the year 1985 there was a major adaptation to the collegiate men’s game of basketball, it was the addition of the shot clock. This would have ripple effects throughout the game for generations to come. Now 30 complete seasons with the shot clock in place; the game has made progression into a national phenomenon. Throughout the country people focus on brackets and the champions, even the POTUS will make a bracket for this event. But I will be focusing on the past champions and the commonality between them to see if the game has made advances over the past 30 seasons. The purpose of this statistical analysis is to see if trends have formed or not within team stats of champions and their counterparts. There are many

If you extrapolate that points percentage over a full 82-game season, that would be a 110-point pace, elite level performance.

The data for this folio was numerical data. The sample size was sufficient as the scores have been averaged out of twenty throws not four or five. Twenty was a good number as if was done that's not enough but if thirty-five or forty was done it gives us to much data and the possibility for an increased number of outliers. This creates problems when creating histograms as the classes range could be too great. Leading to the data not being shown in great detail. Patrick had two outliers while Angus had zero outliers. The outliers affected Patrick’s results because the two outliers increased his average to a higher score. Without Patrick’s two outliers his mean or average would have been 96.61 instead of 121.85. This shows how much Patrick’s two outliers made a difference to his average. The outliers also made a difference to his range. His range would have been 180 - 12 = 168 instead of 336 - 12 = 324. Some of the centre for Patrick’s data has been effected by the outliers and so has the