Client Report - Finding Relationships in Baseball (Project 3)

Course DS 250

Author

Brayden McAllister

Elevator pitch

paste your elevator pitch here A SHORT (4-5 SENTENCES) PARAGRAPH THAT DESCRIBES KEY INSIGHTS TAKEN FROM METRICS IN THE PROJECT RESULTS THINK TOP OR MOST IMPORTANT RESULTS.

Read and format project data 
# Include and execute your code here
df = pd.read_csv("https://github.com/byuidatascience/data4names/raw/master/data-raw/names_year/names_year.csv")

Highlight the Questions and Tasks

QUESTION|TASK 1

Write an SQL query to create a new dataframe about baseball players who attended BYU-Idaho. The new table should contain five columns: playerID, schoolID, salary, and the yearID/teamID associated with each salary. Order the table by salary (highest to lowest) and print out the table in your report.

type your results and analysis here

Read and format data 
# Include and execute your code here

include figures in chunks and discuss your findings in the figure.

::: {#cell-Q1 chart .cell execution_count=4}

plot example 
# Include and execute your code here
chart = px.bar(df.head(200),
    x="name", 
    y="AK"
)
chart.show()

My useless chart

:::

::: {#cell-Q1 table .cell .tbl-cap-location-top tbl-cap=‘Not much of a table’ execution_count=5}

table example 
# Include and execute your code here
mydat = df.head(1000)\
    .groupby('year')\
    .sum()\
    .reset_index()\
    .tail(10)\
    .filter(["year", "AK","AR"])

display(mydat)
year AK AR
96 2006 21.0 183.0
97 2007 28.0 153.0
98 2008 36.0 212.0
99 2009 34.0 179.0
100 2010 22.0 196.0
101 2011 41.0 148.0
102 2012 28.0 140.0
103 2013 26.0 134.0
104 2014 20.0 114.0
105 2015 28.0 121.0

:::

QUESTION|TASK 2

This three-part question requires you to calculate batting average (number of hits divided by the number of at-bats)

Write an SQL query that provides playerID, yearID, and batting average for players with at least 1 at bat that year. Sort the table from highest batting average to lowest, and then by playerid alphabetically. Show the top 5 results in your report.

Use the same query as above, but only include players with at least 10 at bats that year. Print the top 5 results.

Now calculate the batting average for players over their entire careers (all years combined). Only include players with at least 100 at bats, and print the top 5 results.

type your results and analysis here

Read and format data 
# Include and execute your code here

include figures in chunks and discuss your findings in the figure.

::: {#cell-Q2 chart .cell execution_count=7}

plot example 
# Include and execute your code here
chart = px.bar(df.head(200),
    x="name", 
    y="AK"
)
chart.show()

My useless chart

:::

::: {#cell-Q2 table .cell .tbl-cap-location-top tbl-cap=‘Not much of a table’ execution_count=8}

table example 
# Include and execute your code here
mydat = df.head(1000)\
    .groupby('year')\
    .sum()\
    .reset_index()\
    .tail(10)\
    .filter(["year", "AK","AR"])

display(mydat)
year AK AR
96 2006 21.0 183.0
97 2007 28.0 153.0
98 2008 36.0 212.0
99 2009 34.0 179.0
100 2010 22.0 196.0
101 2011 41.0 148.0
102 2012 28.0 140.0
103 2013 26.0 134.0
104 2014 20.0 114.0
105 2015 28.0 121.0

:::

QUESTION|TASK 3

Pick any two baseball teams and compare them using a metric of your choice (average salary, home runs, number of wins, etc). Write an SQL query to get the data you need, then make a graph using Plotly Express to visualize the comparison. What do you learn?

type your results and analysis here

Read and format data 
# Include and execute your code here

include figures in chunks and discuss your findings in the figure.

::: {#cell-Q3 chart .cell execution_count=10}

plot example 
# Include and execute your code here
chart = px.bar(df.head(200),
    x="name", 
    y="AK"
)
chart.show()

My useless chart

:::

::: {#cell-Q3 table .cell .tbl-cap-location-top tbl-cap=‘Not much of a table’ execution_count=11}

table example 
# Include and execute your code here
mydat = df.head(1000)\
    .groupby('year')\
    .sum()\
    .reset_index()\
    .tail(10)\
    .filter(["year", "AK","AR"])

display(mydat)
year AK AR
96 2006 21.0 183.0
97 2007 28.0 153.0
98 2008 36.0 212.0
99 2009 34.0 179.0
100 2010 22.0 196.0
101 2011 41.0 148.0
102 2012 28.0 140.0
103 2013 26.0 134.0
104 2014 20.0 114.0
105 2015 28.0 121.0

:::

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