A Roster Construction Tool You Can Use While Drafting

Diversity is good in all places. In the natural world, in the workplace, in your neighborhood, a collection of diverse parts makes the sum stronger. In fantasy baseball, that means your team has power hitters, base stealers, contact hitters, ironmen, rotation horses, flame throwers, and AI robo-mutant zombies that eat pine tar for breakfast and rosin for dinner. Ok, that last one may have taken things too far. The point is, if you’re only drafting for overall value (easy to do), you could end up with a lopsided team. Sure, you’ll win the home run category, but that’s only 12 points. You need to diversify and here’s one way you can do that during your next draft.

Head to the auction calculator, plug in your league specific settings (number of teams, roster requirements, positional eligibility, projection system, etc.,) and then export two csv files, one for hitters and one for pitchers. You can combine them and sort it by value, but that takes an extra step. You can either bring your csv into excel or numbers or you can use the python code below. Don’t be afraid of reading the code below even if you’ve never seen python code. It’s very intuitive:

# A list of the batters you’ve drafted
my_batters=['Shohei Ohtani','Matt Olson','William Contreras','Dansby Swanson','Ketel Marte','Jarren Duran']

# Isolate your auction calculator .csv output (or dataframe) to only the hitters you drafted.
B=df.loc[df['Name'].isin(my_batters)]

# Create a bar chart visual with the python package, “Matplotlib”.
y_axis = ['RBI', 'R', 'SB', 'HR', 'AVG']
x_axis = [sum(B['mW or mRBI'].astype(float)), sum(B['mSV or mR'].astype(float)),
sum(B['mERA or mSB'].astype(float)), sum(B['mWHIP or mHR'].astype(float)), sum(B['mSO or mAVG'].astype(float))]
plt.barh(y_axis,x_axis)
plt.title('Drafted Hitters')
plt.ylabel('Category')
plt.xlabel('$Value')
plt.show()


This will output a very simple bar chart, it doesn’t need to be pretty, that will give you a sense of how your roster is being constructed as you draft:

The example above is coming from my TGFBI league. By value, I would have drafted Teoscar Hernández instead of Jarren Duran. But I knew my team was lacking stolen bases and I needed to do something about it. We could argue about draft strategy and value and opportunity cost and all kinds of other things, but for the point of this exercise, just take the visual for what it is. It is a way to visually represent your team’s category distribution, allowing you to make more informed decisions while drafting.

If you’re not down with python, you can still do this in excel and/or numbers. Here’s how:

• 1. open your output file in numbers/excel.
• 2. open a second tab where you can copy and paste the rows of the players you draft or plan to draft.
• 3. aggregate (I chose sum) the category value scores (mHR, mR, mSB, mAVG, mRBI) in a new row.
• 4. create a bar chart of the aggregated row with categories on one axis and the aggregated values on the other.

This is particularly useful when you are in a draft and you’re debating between two players. Take my example above. My next pick will be swinging around soon and I am still lacking stolen bases. I like Christopher Morel for the power he could add to my team. But, I also like Tommy Edman as he could help even out the bars in my bar chart with his stolen base potential. Hopefully this article gets published after I make my pick. Using this in-draft tool, I can make better decisions. Note that I’m using hitters as the example, but the exact same process can be applied to pitchers.

Maybe you’re like me and you don’t want to spend even more money on draft-assistant software. Maybe you think, “I could just build my own”. If so, try this quick and easy in-draft tool. Most of the work is already done for you by the auction calculator. Take on a good mock draft and work your way through this process along the way in order to practice using it before the big time.