How To Create A Chess Study Plan

  • Post author:
  • Post category:General
  • Reading time:14 mins read

Creating a chess study plan is an often overlooked aspect to chess improvement. ChessGoals seeks to help you reach your goals with data-driven study plans designed for players of all levels.

Effectiveness and Efficiency


The two big ones for chess improvement. How many hours are spent on chess, and how are those hours allocated. Below is a plot of annual rating gain on the y-axis and weekly hours spent on chess on the x-axis. There are other variables confounded with rating gains, but there are a few key takeaways on time spent.

Annual Rating Gain vs Chess Hours/Week
  • The most valuable hours spent on chess are going from 0 to about 5 hours in terms of rating gains per hour.
  • Increasing time above 5 hours per week is helpful as long as burnout is avoided.
  • There appears to be a point around 20 hours/week that studying more chess is not helpful anymore.


The last section may be intuitive, studying more chess tends to lead to higher rating gains. The next sets of analysis could use a larger sample size to make definitive conclusions, but there are some definite trends in the data. This plot shows annual rating gain on the y-axis versus % of time chess time spent playing on the x-axis (example: 0.6 = 60%). There are a few main takeaways from this chart.

Study Plan Rating Gain Plot
Annual Rating Gain vs % of Time Playing
  • Novice and Beginner players (<1100, red line) gain the most points on average, and the rating gains become harder as rating goes up.
  • Beginner players stand to benefit from spending a lot of time playing chess. I did a blog post documenting an extreme approach on how beginners can gain 400 rating points in a year by playing 97% of the time.
  • At the higher rating bands there should be more time reserved for studying.

Linear Regression to Predict Rating Gain

The post titled The Effect of Age on Chess Improvement documented that younger age, lower rating, and more time spent on chess were all correlated with larger increases in rating gain. An interesting way to look at chess progress is to predict how many rating points we think a player will gain based on their three input factors above, and then take the difference of actual gain minus predicted gain. We will call this variable out-performance, or ‘outperform’ for short. To check the validity of the model, we can compare the outperform variable against the 3 input factors.


If the model is doing a good job, we’d expect the outperform variable to center around 0 for all ratings. The blue lines does bounce around a little bit, but tends to be pretty close to 0 with no obvious problems. Lower ratings have a lot more noisiness, which we expect since they tend to gain more rating points on average.

Outperform vs Start Rating


One minor regret I have on this survey was asking for age in categories. The original reason was to keep the information feeling more anonymous for the survey respondents. Now I wish the variable was continuous age so we can differentiate for example a 47 year old from a 41 year old. This plot also looks pretty good across the spectrum of ages.

Outperform vs Age

Chess Hours Per Week

This plot is a bit wavier, but I don’t notice any strong trends. Hours per week is an extremely noisy predictor on rating gain, which surprised me when I first started analyzing ChessGoals data. Intuitively, I thought that it would be the most important predictor. It is important, but it’s third to start rating (1) and age (2). When I designed the ChessGoals Gain Calculator I use start rating and age to build an equation that maps hours to rating gain.

Outperform vs Chess Hours Per Week

Decision Trees to Parse the Data

Decision trees are a fun area of statistics that can find clusters of data that have similar characteristics. For the initial decision tree, I looked at the following equation:

gains ~ startrating + age + hours + Play_p + analyzep + tacticsp + openingsp + endgamesp + strategyp

This means we allowed for the three main factors (start rating, age, and hours) in addition to the percentage of time on each aspect of the game. Lets first look at how to read a decision tree plot.

How to Interpret a Decision Tree Plot

Start at the top. The 149 is the average rating gain for the whole list of ChessGoals survey respondents. This is on 100% of the data since we are at the very top. The best predictor to dichotomize rating gain is start rating greater or equal to 1097. For players greater or equal to 1097, move to the left [yes] side of the image. This accounts for 72% of the players, and their average annual rating gain was 98 points. For players below 1097 [no], which is 28% of the players, they gain 278 points on average.

Sample Split

Designing Study Plan Groups

This is one of my favorite plots coming out of ChessGoals. Why do I choose favorites when it comes to plots? I’m not really sure and it’s probably unhealthy…

Study Plan Decision Tree

The first two levels of the decision tree split our players into the following categories:

  • <821 rating (12% of the players, average gain 369 points)
  • 821-1096 rating (17% of the players, average gain 215 points)
  • 1097-1750 rating (52% of the players, average gain 121 points)
  • 1751+ rating (19% of the players, average gain 37 points)

Study Plans

This alignment was the deciding factor in creating the six study plan categories.

ChessGoals Study Plans
Study Plan Categories

The six study plan categories can also be rolled up into three levels: <1100, 1100-1699, 1700+. We will now look at some key aspects of the study plans for each rating category.

Novice (<800)

Novice players tend to be either new to chess, or have played casually without having a long-term study plan. One very interesting fact comes out of the novice decision tree.

Novice Decision Tree

The decision tree shows one very important fact for novices, the importance of analyzing games. In our Novice Study Plans we recommend spending around 20% of the time doing game analysis. It’s important for novice players to build good habits, and that’s where the recommendation comes from to spend time reviewing games.

Beginner (800-1099)

At the beginner level there appear to be two approaches to rating gain. The main recommendation for beginners is to take a balanced approach. The balanced approach is to play about 65% of the time, tactics 15%, analysis 10%, and the remaining 10% split between other activities. The second approach is to go for what we called the speed runner above, and that’s 97% of the time playing with the remaining 3% quickly reviewing those games.

Intermediate (1100-1699)

Combining the two intermediate categories since the recommendations are so similar. This chart is very confusing, but I’ll list a few key takeaways.

Intermediate Decision Tree
  1. Do not spend too much time on openings. Every split on openings shows spending a lower percentage of total times on openings is beneficial. We recommend only spending 2.5% of the time on openings. Above 1700 it becomes more important.
  2. Play a lot of games. Spending about 75% of the time playing games seems to be a sweet spot for intermediates.
  3. Split the 25% of time spent studying among other activities. In order of importance: tactics, analysis, strategy, endings, openings

The intermediate premium study plan is the most popular item in the ChessGoals shop.

Advanced (1700-1999)

Advanced players need to start playing a bit less and studying more.

Advanced Decision Tree

The first split in the advanced group says that playing over 86% of the time leads to outperformance of -97, i.e. under performing expectations. Here are the main recommendations at the advanced level:

  1. Play chess about 60% of the time, starting to add in some faster time control games since they have had time to develop faster intuition.
  2. Follow the guide of Yusupov’s Training Plan.
  3. Have a strategy resource and stick with it. lessons or Reassess Your Chess.
  4. Use the WoodPecker method for tactics.
  5. Add more opening study and build a repertoire.

Expert (2000-2300)

This is a tricky group to create a study plan. The sample size is smaller, so much of the plan is based on a combination of the data we have, my own intuition, and digging into specific plans that worked for experts.

We have a free expert study plan page that recommends resources for 2000+ players. There is also the personal study consult with myself to receive a custom 12-week study plan and similarity report. I was able to push my USCF rating from the low 2100s USCF up about 100 points as an adult and can help experts on their quest to do the same.

Next Steps

We have three options to take your study plan to the next level.

1. Premium Study Plans for sale

12-week premium study plans PDFs with weekly reminders, email support and a community of others working to reach their chess goals.

ChessGoals Premium Study Plans
Premium Study Plans

2. Study Plan Worksheets

Take the guess work out of chess improvement. Get your FREE 12 week study plan course.

By signing up for the newsletter, you receive a link to access our free study plan worksheets.

3. Free Study Plan Information

We always want to keep some free content available for users. We have 6 different free study plan pages that break down different information and resources.

This Post Has One Comment

Comments are closed.