A Step-by‑Step Guide to Decoding IndyCar Telemetry for Aspiring Engineers

Ever stared at a wall of numbers on a screen and thought, “What on earth does any of this mean?” You’re not alone. At Open Wheel Insights we’ve all been there—whether you’re watching a race or tinkering with a data logger in the garage. In this post I’ll walk you through a simple, no‑frills process to turn raw IndyCar telemetry into insights you can actually use. Grab a coffee, fire up your laptop, and let’s demystify those data streams together.

Why telemetry matters

Telemetry is the pulse of the car. It tells you how the engine, brakes, suspension and driver are interacting in real time. For an aspiring engineer, learning to read telemetry is like learning a new language—one that lets you speak directly to the machine. At Open Wheel Insights we treat telemetry as a conversation: you ask the car a question, the data gives you an answer, and you decide what to do next.

Key data points you’ll see

Speed – vehicle velocity in mph or km/h.
Throttle position – how far the driver is opening the pedal, expressed as a percentage.
Brake pressure – hydraulic pressure applied to the brakes.
Steering angle – how many degrees the wheel is turned.
G‑force – lateral and longitudinal forces measured in g’s.
Engine RPM – revolutions per minute, the heartbeat of the power unit.

Knowing what each column means is half the battle. The other half is learning how to clean, visualize and interpret the numbers.

Step 1: Get the data

IndyCar teams usually provide telemetry in CSV or binary formats. If you’re just starting out, look for publicly released CSV files from race weekends or the IndyCar Fan Experience portal. Save the file in a dedicated folder on your computer—something like openwheelinsights/telemetry/2024_race1.csv.

Tip: Keep a simple naming convention. It helps later when you write scripts that loop through multiple sessions.

Step 2: Load it into a friendly tool

You don’t need a fancy data‑science platform to begin. Excel, Google Sheets, or even a free Python notebook will do. For this guide I’ll use Python because it’s reproducible, but feel free to open the CSV in Excel if you prefer a visual interface.

import pandas as pd

df = pd.read_csv('2024_race1.csv')
print(df.head())

If the first few rows look scrambled, you might need to specify a separator (sep=',') or encoding (encoding='utf‑8'). Most public files are clean, but a quick peek at df.head() will confirm.

Step 3: Clean up the data

Raw telemetry often contains:

Missing values – gaps when a sensor momentarily dropped out.
Noise – tiny fluctuations that don’t reflect real changes.

A simple way to handle missing values is to forward‑fill them:

df = df.fillna(method='ffill')

For noise, apply a rolling average. A window of 5 samples works well for most high‑frequency data:

df['speed_smooth'] = df['speed'].rolling(window=5).mean()

You now have a cleaner set of columns you can trust.

Step 4: Visualize the basics

Seeing is believing. Plotting speed against time instantly shows you where the car accelerated, braked, or coasted.

import matplotlib.pyplot as plt

plt.figure(figsize=(10,4))
plt.plot(df['time'], df['speed_smooth'], label='Speed (smoothed)')
plt.xlabel('Time (s)')
plt.ylabel('Speed (mph)')
plt.title('Speed vs Time')
plt.legend()
plt.show()

Do the same for throttle and brake pressure. Overlaying them on the same graph reveals the driver’s style. A friendlier way to do this in Excel is to insert a line chart and add multiple series.

Step 5: Spot the key moments

Now that you have clean charts, look for patterns:

Hard braking zones – high brake pressure combined with a rapid drop in speed.
Throttle lift‑offs – throttle position falling to near zero while speed is still climbing, indicating a coasting phase.
Corner entry – a spike in lateral G‑force just before a turn, followed by a peak in steering angle.

Mark these timestamps in a separate column called event. For example:

df['event'] = ''
df.loc[(df['brake_pressure'] > 80) & (df['speed_smooth'].diff() < -5), 'event'] = 'hard_brake'

Now you can filter the dataframe to see only the moments you care about:

hard_brakes = df[df['event'] == 'hard_brake']
print(hard_brakes[['time','speed_smooth','brake_pressure']])

Step 6: Compare driver to baseline

If you have telemetry from a seasoned driver on the same track, you can benchmark. Load the baseline file, align the time axes, and compute differences.

baseline = pd.read_csv('2024_race1_pro.csv')
merged = pd.merge_asof(df.sort_values('time'), baseline.sort_values('time'), on='time', suffixes=('_my','_pro'))
merged['speed_diff'] = merged['speed_smooth_my'] - merged['speed_smooth_pro']

Plot the speed difference to see where you’re faster or slower. Small gaps in corner entry often point to suspension tuning or driving technique improvements.

Step 7: Turn insights into action

At this point you have a list of concrete observations:

Late braking – your hard brakes start 0.3 seconds later than the pro.
Throttle lag – you keep the throttle open a second longer after turn‑in.
Steering smoothness – your steering angle spikes more sharply.

Each point suggests a simple experiment. For late braking, try adjusting the brake bias or practice a “trail‑brake” technique in a simulator. For throttle lag, experiment with a shorter shift schedule. And for steering smoothness, work on smooth inputs or explore a different wheel rack ratio.

Document each change, capture new telemetry, and repeat the analysis. Over a few iterations you’ll see measurable gains—sometimes as little as a tenth of a second per lap, which matters a lot in IndyCar.

Quick checklist for your next session

☐ Download the CSV and store it with a clear name.
☐ Load into Python or Excel and inspect the first rows.
☐ Fill missing values and apply a rolling average.
☐ Plot speed, throttle, brake pressure, and G‑force.
☐ Flag hard brakes, throttle lift‑offs, and corner entries.
☐ Compare against a baseline if you have one.
☐ Write down three actionable items and test them.

By following these steps, you’ll move from staring at a confusing spreadsheet to having a clear roadmap for improvement. That’s the power of telemetry, and that’s what we love to share at Open Wheel Insights—simple tools that let you get closer to the car and the sport you love.

Happy data hunting, and may your next lap be smoother, faster, and more fun.